Package ‘epiDisplay’
November 3, 2015
Version 3.2.2.0
Date 2015-11-01
Title Epidemiological Data Display Package
Author Virasakdi Chongsuvivatwong <[email protected]>
Maintainer Virasakdi Chongsuvivatwong <[email protected]>
Depends R (>= 2.6.2), foreign, survival, MASS, nnet
Suggests
Description Package for data exploration and result presentation.
Full 'epicalc' package with data management functions is available
at the author's repository.
License GPL (>= 2)
URL http://CRAN.R-project.org/
Repository CRAN
Date/Publication 2015-11-03 01:02:29
NeedsCompilation no
R topics documented:
Age at marriage . . . . . . .
aggregate numeric . . . . . .
aggregate plot . . . . . . . .
Air Pollution . . . . . . . .
alpha . . . . . . . . . . . . .
ANC Table . . . . . . . . .
Antenatal care data . . . . .
Attitudes dataset . . . . . . .
Bangladesh Fertility Survey .
Blood pressure . . . . . . .
Cancer survival . . . . . . .
cc . . . . . . . . . . . . . .
CI . . . . . . . . . . . . . .
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3
4
6
9
9
12
12
13
14
14
15
16
18
R topics documented:
2
Codebook . . . . . . . . . . .
Data for cleaning . . . . . . .
des . . . . . . . . . . . . . . .
DHF99 . . . . . . . . . . . .
dotplot . . . . . . . . . . . . .
Ectopic pregnancy . . . . . . .
Familydata . . . . . . . . . .
Follow-up Plot . . . . . . . .
Hakimi’s data . . . . . . . . .
Hookworm 1993 . . . . . . .
Hookworm and blood loss . .
IUD trial admission data . . .
IUD trial discontinuation data
IUD trial follow-up data . . .
kap . . . . . . . . . . . . . . .
List non-function objects . . .
lookup . . . . . . . . . . . . .
lrtest . . . . . . . . . . . . . .
Matched case-control study . .
matchTab . . . . . . . . . . .
mhor . . . . . . . . . . . . . .
Montana . . . . . . . . . . . .
Oswego . . . . . . . . . . . .
Outbreak investigation . . . .
poisgof . . . . . . . . . . . .
Power . . . . . . . . . . . . .
print alpha . . . . . . . . . . .
print cci . . . . . . . . . . . .
print des . . . . . . . . . . . .
print kap.ByCategory . . . . .
print kap.table . . . . . . . . .
print lrtest . . . . . . . . . . .
print matchTab . . . . . . . .
print n.for.2means . . . . . . .
print n.for.2p . . . . . . . . .
print n.for.cluster.2means . . .
print n.for.cluster.2p . . . . . .
print n.for.equi.2p . . . . . . .
print n.for.lqas . . . . . . . . .
print n.for.noninferior.2p . . .
print n.for.survey . . . . . . .
print power.for.2means . . . .
print power.for.2p . . . . . . .
print statStack . . . . . . . . .
print summ.data.frame . . . .
print summ.default . . . . . .
print tableStack . . . . . . . .
pyramid . . . . . . . . . . . .
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21
22
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Age at marriage
3
Risk.display . . . . . . . . . . . . . . .
ROC . . . . . . . . . . . . . . . . . . .
sampsize . . . . . . . . . . . . . . . . .
setTitle . . . . . . . . . . . . . . . . .
shapiro.qqnorm . . . . . . . . . . . . .
Sleepiness . . . . . . . . . . . . . . . .
statStack . . . . . . . . . . . . . . . . .
summ . . . . . . . . . . . . . . . . . .
tab1 . . . . . . . . . . . . . . . . . . .
tableStack . . . . . . . . . . . . . . . .
tabpct . . . . . . . . . . . . . . . . . .
Timing exercise . . . . . . . . . . . . .
titleString . . . . . . . . . . . . . . . .
Tooth decay . . . . . . . . . . . . . . .
Voluntary counselling and testing . . . .
Xerophthalmia and respiratory infection
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Index
Age at marriage
Dataset on age at marriage
This dataset contains data on age at first marriage of attendants at a workshop in 1997.
Usage
data(Marryage)
Format
A data frame with 27 observations on the following 7 variables.
id a numeric vector
sex a factor with levels male female
birthyr a numeric vector indicating year of birth
educ a factor with levels bach- bachelor or higher
marital a factor with levels Single Married
maryr a numeric vector indicating year of marriage
endyr a numeric vector indicating year of analysis
data(Marryage)
des(Marryage)
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63
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Description
Examples
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4
aggregate numeric
aggregate numeric
Summary statistics of a numeric variable by group
Description
Split the numeric variable into subsets, compute summary statistics for each, and return the results
in a data frame.
Usage
## S3 method for class 'numeric'
aggregate(x, by, FUN=c("count","sum","mean","median","sd","se","min","max"),
na.rm=TRUE, length.warning=TRUE, ...)
Arguments
x
a numeric variable
by
a list of grouping elements, each as long as the variables in ’x’. Names for the
grouping variables are provided if they are not given. The elements of the list
will be coerced to factors (if they are not already factors).
FUN
scalar functions to compute the summary statistics which can be applied to all
data subsets.
na.rm
whether missing values will be removed during the computation of the statistics.
length.warning show warning if x has any missing values
...
additional arguments passed on to ’aggregate’
Details
This is the ’aggregate’ method for objects inheriting from class ’numeric’.
If Epicalc is loaded, applying ’aggregate’ to a numeric variable ’x’ will call ’aggregate.numeric’. If
’x’ is a data frame, ’aggregate.data.frame’ will be called.
If the Epicalc package is not loaded, ’aggregate’, from the stats package, coerces numeric variables
(including ’ts’ objects) into a data frame and calls ’aggregate.data.frame’.
The ’FUN’ argument in ’aggregate.data.frame’ can accept only one function.
’aggregate.numeric’ takes a different approach. More than one function can be suppplied to the
’FUN’ argument, however it can only be applied to one numeric variable.
’aggregate’ in Epicalc is ’backward compatible’ with the ’aggregate’ function from the stats package. In other words, Epicalc users do not need to change basic syntax or arguments. However, the
naming system of the returned object is slightly different. In addition to the ability to provide more
statistics in one command, another useful feature of ’aggregate.numeric’ in Epicalc is the default
values of FUN. Without typing such an argument, ’aggregate.numeric’ gives commonly wanted
statistics in a shorter line of command.
aggregate numeric
5
Note that ’na.rm’ set to TRUE by default to allow computation of descriptive statistics such as
’mean’, and ’sd’, when they are in the FUN argument, and ’length’ is computed with missing
records included. In standard R functions, the equivalent argument is ’"na.rm"=TRUE’.
The default value of the argument ’length.warning’ is TRUE. A condition where ’x’ has any missing
value will be noticed, which is useful during data exploration. In further analysis, after missing
values have been recognized, users may change ’length.warning’ to FALSE to make the output
look nicer. Both ’na.rm’ and ’length.,warning’ will have no effect if there are no missing values in
x.
’count’ is an additional function specific to ’aggregate.numeric’. It displays the number of nonmissing records in each subgroup.
’aggregate.plot’ makes use of the above function in drawing bar plots with error lines computed
from ’aggregate.numeric’. When ’FUN="mean"’, the automactic choice of error values is "se".
Users can also choose "sd" or "ci". ’alpha’ is effective only for ’error="ci"’. If ’FUN="median"’,
the error values are inter-quartile range.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’aggregate’, ’summ’ and ’tapply’
Examples
data(Compaq)
.data <- Compaq
attach(.data)
## If 'x' is a data frame, the default S3 aggregate method from the stats package is called.
aggregate(data.frame(id,year), by=list(HOSPITAL=hospital, STAGE=stage),
FUN="mean")
# The two additional columns are means of 'id' and 'year'
## If 'x' is a numeric vector, 'aggregate.numeric' from Epicalc package is called.
aggregate(year, by = list(HOSPITAL = hospital, STAGE = stage),
FUN = mean)
# The above command is the same as the one below.
# However, note the difference in the name of the last column of the returned
# data frame.
aggregate.data.frame(year, by = list(HOSPITAL = hospital,
STAGE = stage), FUN = mean)
# aggregate in Epicalc can handle multiple functions
aggregate(year, by = list(HOSPITAL = hospital, STAGE = stage),
FUN = c("mean", "sd", "length"))
## Handling of missing values
.data$year[8] <- NA
detach(.data)
6
aggregate plot
attach(.data)
aggregate(year, by = list(STAGE = stage), FUN = c("length", "count"))
# Note the difference between 'length' and 'count' in Stage 1
# Means of subsets in 'aggregrate.data.frame'
# have 'na.rm' set to FALSE.
aggregate.data.frame(year, by = list(STAGE = stage), FUN = "mean")
## The default value of 'na.rm' is TRUE in aggregate.numeric of Epicalc.
aggregate(year, by = list(STAGE = stage), FUN = c("mean","median"))
## It can be set to FALSE though.
aggregate(year, by = list(STAGE = stage), FUN = c("mean","median"),
"na.rm"=FALSE)
# Omitting the FUN argument produces various statistics.
options(digits=3)
aggregate(year, by = list(HOSPITAL = hospital, STAGE = stage))
# Warning of na.rm
aggregate(year, by = list(HOSPITAL = hospital, STAGE = stage), length.warning=FALSE)
# Newly defined functions can be used
p05 <- function(x) quantile(x, prob=.05, na.rm=TRUE)
p95 <- function(x) quantile(x, prob=.95, na.rm=TRUE)
aggregate(year, by = list(HOSPITAL = hospital, STAGE = stage), FUN=c("p05", "p95"))
detach(.data)
rm(list=ls())
aggregate plot
Plot summary statistics of a numeric variable by group
Description
Split a numeric variable into subsets, plot summary statistics for each
Usage
## S3 method for class 'plot'
aggregate(x, by, grouping = NULL, FUN = c("mean", "median"),
error = c("se", "ci", "sd", "none"), alpha = 0.05, lwd = 1,
lty = "auto", line.col = "auto", bin.time = 4, bin.method = c("fixed",
"quantile"), legend = "auto", legend.site = "topright",
legend.bg = "white", xlim = "auto", ylim = "auto", bar.col = "auto",
cap.size = 0.02, lagging = 0.007, main = "auto", return.output = FALSE, ...)
Arguments
x
a numeric variable
aggregate plot
7
by
a list of grouping elements for the bar plot, or a single numeric or integer variable
which will form the X axis for the time line graph
grouping
further stratification variable for the time line graph
FUN
either "mean" or "median"
error
statistic to use for error lines (either ’se’ or ’sd’ for barplot, or ’ci’ or ’none’ for
time line graph). When FUN = "median", can only be ’IQR’ (default) or ’none’.
alpha
level of significance for confidence intervals
lwd
relative width of the "time" lines. See ’lwd’ in ?par
lty
type of the "time" lines. See ’lty’ in ?par
line.col
colour(s) of the error and time lines
bin.time
number bins in the time line graph
bin.method
method to allocate the "time" variable into bins, either with ’fixed’ interval or
equally distributed sample sizes based on quantiles
legend
presence of automatic legend for the time line graph
legend.site
a single character string indicating location of the legend. See details of ?legend
legend.bg
background colour of the legend
xlim
X axis limits
ylim
Y axis limits
bar.col
bar colours
cap.size
relative length of terminating cross-line compared to the range of X axis
lagging
lagging value of the error bars of two adjecant categories at the same time point.
The value is result of dividing this distance with the range of X axis
main
main title of the graph
return.output
whether the dataframe resulted from aggregate should be returned
...
additional graphic parameters passed on to other methods
Details
This function plots aggregated values of ’x’ by a factor (barplot) or a continuous variable (time line
graph).
When ’by’ is of class ’factor’, a bar plot with error bars is displayed.
When ’by’ is a continuous variable (typically implying time), a time line graph is displayed.
Both types of plots have error arguments. Choices are ’se’ and ’sd’ for the bar plot and ’ci’ and IQR
for both bar plot and time line graph. All these can be suppressed by specifying ’error’="none".
’bin.time’ and ’bin.method’ are exclusively used when ’by’ is a continuous variable and does not
have regular values (minimum frequency of ’by’ <3). This condition is automatically and silently
detected by ’aggregate.plot’ before ’bin.method’ chooses the method for aggregation and bin.time
determines the number of bins.
If ’legend = TRUE" (by default), a legend box is automatically drawn on the "topright" corner of the
graph. This character string can be changed to others such as, "topleft", "center", etc (see examples).
’cap.size’ can be assigned to zero to remove the error bar cap.
8
aggregate plot
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’aggregate.data.frame’, ’aggregate.numeric’, ’tapply’
Examples
data(Compaq)
.data <- Compaq
attach(.data)
aggregate.plot(x=year, by=list(HOSPITAL = hospital, STAGE = stage),
return = TRUE)
# moving legend and chaging bar colours
aggregate.plot(x=year, by=list(HOSPITAL = hospital, STAGE = stage), error="ci",
legend.site = "topleft", bar.col = c("red","blue"))
detach(.data)
# Example with regular time intervals (all frequencies > 3)
data(Sitka, package="MASS")
.data <- Sitka
attach(.data)
tab1(Time, graph=FALSE) # all frequencies > 3
aggregate.plot(x=size, by=Time, cap.size = 0) # Note no cap on error bars
# For black and white presentation
aggregate.plot(x=size, by=Time, grouping=treat, FUN="median",
line.col=3:4, lwd =2)
detach(.data)
# Example with irregular time intervals (some frequencies < 3)
data(BP)
.data <- BP
attach(.data)
des(.data)
age <- as.numeric(as.Date("2008-01-01") - birthdate)/365.25
aggregate.plot(x=sbp, by=age, grouping=saltadd, bin.method="quantile")
aggregate.plot(x=sbp, by=age, grouping=saltadd, lwd=3,
line.col=c("blue","green") , main = NULL)
title(main="Effect of age and salt adding on SBP", xlab="years",ylab="mm.Hg")
points(age[saltadd=="no"], sbp[saltadd=="no"], col="blue")
points(age[saltadd=="yes"], sbp[saltadd=="yes"], pch=18, col="green")
detach(.data)
rm(list=ls())
## For a binary outcome variable, aggregrated probabilities is computed
data(Outbreak)
.data <- Outbreak
attach(.data)
.data$age[.data$age == 99] <- NA
detach(.data)
attach(.data)
Air Pollution
9
aggregate.plot(diarrhea, by=age, bin.time=5)
diarrhea1 <- factor(diarrhea)
levels(diarrhea1) <- c("no","yes")
aggregate.plot(diarrhea1, by=age, bin.time=5)
detach(.data)
rm(list=ls())
Air Pollution
Dataset on air pollution and deaths in UK
Description
Deaths in London from 1st-15th Dec 1952
Usage
data(SO2)
Format
A data frame with 15 observations on the following 4 variables.
day a numeric vector: the day in Dec 1952
deaths a numeric vector: number of deaths
smoke a numeric vector: atmospheric smoke (mg/cu.m)
SO2 a numeric vector: atmospheric sulphur dioxide (parts/million)
Source
from John F. Osborn, Statistical Exercises in Medical Research, Blackwell 1979
alpha
Cronbach’s alpha
Description
Calculate reliability coefficient of items in a data frame
Usage
alpha (vars, dataFrame, casewise = FALSE, reverse = TRUE,
decimal = 4, vars.to.reverse = NULL, var.labels = TRUE,
var.labels.trunc =150)
alphaBest (vars, dataFrame, standardized = FALSE)
10
alpha
Arguments
vars
a vector containing at least three variables from the data frame
dataFrame
data frame where items are set as variables
casewise
whether only records with complete data will be used
reverse
whether item(s) negatively correlated with other majority will be reversed prior
to computation
decimal
number of decimal places displayed
var.labels
presence of descriptions of variables in the last column of the output
var.labels.trunc
number of characters used for variable descriptions, long labels can be truncated
vars.to.reverse
variable(s) to reverse prior to computation
standardized
whether choosing the best subset of items is based on the standardized alpha
coefficient, if FALSE then the unstandardized alpha coefficient is used
Details
This function is based on the ’reliability’ function from package ’Rcmdr’, which computes Cronbach’s alpha for a composite scale.
There must be at least three items in ’vars’ specified by their names or their index in the data frame.
The argument ’reverse’ (default = TRUE) automatically reverses items negatively correlated with
other majority into negative and reports the activities in the first column of the last result section.
This can be overwritten by the argument ’vars.to.reverse’
Similar to the ’reliability’ function, users can see the effect of removing each item on the coefficents
and the item-rest correlation.
’alphaBest’ is a variant of ’alpha’ for successive removal of items aiming to reach the highest
possible Cronbach alpha. The resultant values include variable indices of excluded and remaining
items, which can be forwarded to ’tableStack’ to achieve total and mean scores of the best selected
items. However, there is no promise that this will give the highest possible alpha. Manual attemps
may also be useful in making comparison.
Value
A list.
’alpha’ returns an object of class "alpha"
alpha
unstandardized alpha coefficient
std.alpha
standardized alpha coefficient
sample.size
sample size
use.method
method for handling missing values
rbar
the average inter-item correlation
items.selected names of variables included in the function
alpha
11
alpha.if.removed
a matrix of unstandardized and standardized alpha coefficients and correlation
of each item with the rest of the items
result
as above but includes a column showing the items that were reversed (if TRUE)
and a column of item description. As a matrix, it could be sent to a spreadsheet
software using ’write.csv’
decimal
decimal places
item.labels
a character vector containing descriptions of the items
’apha.Best’ returns a list of the following elements
best.alpha
the possible highest alpha obtained from the function
removed
indices of items removed by the function
remaining
indices of the remaining items
items.reversed names of items reversed
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’cronbach’ from ’psy’ package and ’reliability’ from ’Rcmdr’ package and ’tableStack’ and ’unclassDataframe’ of Epicalc
Examples
data(Cars93, package="MASS")
.data <- Cars93
attach(.data)
alpha(vars=c(Min.Price:MPG.highway, EngineSize), .data)
detach(.data)
data(Attitudes)
.data <-Attitudes
attach(.data)
alpha(qa1:qa18, .data) # Needs full screen of Rconsole
alpha(qa1:qa18, var.labels.trunc=30, .data)
# Fits in with default R console screen
alpha(qa1:qa18, reverse=FALSE, .data)
alphaBest(qa1:qa18, .data) -> best.alpha
best.alpha # .7621
tableStack(best.alpha$remaining, dataFrame=.data, reverse=TRUE)
# Manual attempts by trial and error give the following
alpha(c(qa1:qa9, qa15,qa18), .data) # .7644
detach(.data)
rm(list=ls())
12
Antenatal care data
ANC Table
Dataset on effect of new ANC method on mortality (as a table)
Description
This dataset contains frequency of various combinations of methods of antenatal care in two clinics
with the outcome being perinatal mortality.
Usage
data(ANCtable)
Format
A data frame with 8 observations on the following 4 variables.
death a numeric vector: 1=no, 2=yes
anc a numeric vector indicating antenatal care type: 1=old 2=new
clinic a numeric vector indicating clinic code: 1=clinic A, 2=clinic B
Freq a numeric vector of frequencies
Examples
data(ANCtable)
glm1 <- glm(death==2 ~ factor(anc) + factor(clinic),weights=Freq, family=binomial, data=ANCtable)
logistic.display(glm1)
glm2 <- glm(death==2 ~ factor(anc) + factor(clinic),weights=Freq, family=binomial, data=ANCtable)
summary(glm2)$coefficients
Antenatal care data
Dataset on effect of new antenatal care method on mortality
Description
This dataset contains records of high risk pregnant women under a trial on new and old methods of
antenatal care in two clinics. The outcome was perinatal mortality.
Usage
data(ANCdata)
Format
A data frame with 755 observations on the following 3 variables.
death a factor with levels no yes
anc a factor with levels old new
clinic a factor with levels A B
Attitudes dataset
Attitudes dataset
13
Dataset from an attitude survey among hospital staff
Description
Survey on attitudes related to services among hospital staff.
Codes for the answers qa1 to qa18 are
1
2
3
4
5
= strongly disagree
= disagree
= neutral
= agree
= strong agree
Usage
data(Attitudes)
Format
A data frame with 136 observations on the following 7 variables.
id identifying code of repondent
sex gender of respondent
dep code of department
qa1 I have pride in my job
qa2 I’m happy to give service
qa3 I feel difficulty in giving service
qa4 I can improve my service
qa5 A service person must have patience
qa6 I would change my job if had the chance
qa7 Devoting some personal time will improve oneself
qa8 Hard work will improve oneself
qa9 Smiling leads to trust
qa10 I feel bad if I cannot give service
qa11 A client is not always right
qa12 Experienced clients should follow the procedure
qa13 A client violating the regulation should not bargain
qa14 Understanding colleagues will lead to understanding clients
qa15 Clients like this place due to good service
14
Blood pressure
qa16 Clients who expect our smiling faces create pressure on us
qa17 Clients are often self-centered
qa18 Clients should be better served
Bangladesh Fertility Survey
Dataset from 1988 Bangladesh Fertility Survey
Description
The file consists of a subsample of 1934 women grouped in 60 districts.
Usage
data(Bang)
Format
A data frame with 1934 observations on the following 7 variables.
woman identifying code of each woman
district identifying code for each district
user 1 = using contraceptive 0 = not using
living.children Number of living children at time of survey
1
2
3
4
= none
=1
=2
= 3 or more
age_mean age of woman in years, centred around the mean
urban Type of region of residence: 1 = urban, 0 = rural
constant constant term = 1
Source
Huq, N. M., and Cleland, J. 1990. Bangladesh Fertility Survey 1989 (Main Report). Dhaka: National Institute of Population Research and Training
Blood pressure
Dataset on blood pressure and determinants
Cancer survival
15
Description
This dataset contains information on the records of 100 adults from a small cross-sectional survey
in 2001 investigating blood pressure and its determinants in a community.
Usage
data(BP)
Format
A data frame containing 100 observations and 6 variables with variable descriptions.
Examples
data(BP)
des(BP)
Cancer survival
Dataset on cancer survival
Description
A dataset on cancer survival checking whether there is a survival difference between cancer patients
in private and public hospitals.
Usage
data(Compaq)
Format
A data frame with 1064 observations on the following 7 variables.
id a numeric vector
hospital a factor with levels Public hospital Private hospital
status a numeric vector
stage a factor with levels Stage 1 Stage 2 Stage 3 Stage 4
agegr a factor with levels <40 40-49 50-59 60+
ses a factor with levels Rich High-middle Poor-middle Poor
year a numeric vector indicating the year of recruitment into the study
Examples
data(Compaq)
des(Compaq)
16
cc
cc
Odds ratio calculation and graphing
Description
Odds ratio calculation and graphing
Usage
cc(outcome, exposure, decimal = 2, cctable = NULL, graph = TRUE,
original = TRUE, design = "cohort", main, xlab = "auto", ylab,
alpha = .05, fisher.or = FALSE, exact.ci.or = FALSE)
cci(caseexp, controlex, casenonex, controlnonex, cctable = NULL,
graph = TRUE, design = "cohort", main, xlab, ylab, xaxis, yaxis,
alpha = .05, fisher.or = FALSE, exact.ci.or = FALSE,decimal = 2 )
cs(outcome, exposure, cctable = NULL, decimal = 2, method="Newcombe.Wilson",
main, xlab, ylab, cex, cex.axis)
csi(caseexp, controlex, casenonex, controlnonex, cctable = NULL,
decimal = 2, method="Newcombe.Wilson")
graph.casecontrol(caseexp, controlex, casenonex, controlnonex,
decimal=2)
graph.prospective(caseexp, controlex, casenonex, controlnonex,
decimal=2)
labelTable(outcome, exposure, cctable = NULL, cctable.dimnames = NULL)
make2x2(caseexp, controlex, casenonex, controlnonex)
Arguments
cctable.dimnames
Dimension names of the variables, usually omitted
decimal
number of decimal places displayed
outcome, exposure
two dichotomous variables
cctable
A 2-by-2 table. If specified, will supercede the outcome and exposure variables
graph
If TRUE (default), produces an odds ratio plot
design
Specification for graph; can be "case control","case-control", "cohort" or "prospective"
caseexp
Number of cases exposed
controlex
Number of controls exposed
casenonex
Number of cases not exosed
controlnonex
Number of controls not exposed
original
should the original table be displayed instead of standard outcome vs exposure
table
main
main title of the graph
cc
17
xlab
ylab
alpha
fisher.or
exact.ci.or
xaxis
yaxis
method
cex.axis
cex
label on X axis
label on Y axis
level of significance
whether odds ratio should be computed by the exact method
whether confidence limite of the odds ratio should be computed by the exact
method
two categories of exposure in graph
two categories of outcome in graph
method of computation for 95 percent limits of risk difference
character expansion factor for graph axis
character expansion factor for text in the graph
Details
’cc’ usually reads in two variables whereas in ’cci’ four number are entered manually. However,
both the variables and the numbers should be omitted if the analysis is directly on a table specified
by ’cctable’.
From both functions, odds ratio and its confidence limits, chisquared test and Fisher’s exact test are
computed. The odds ratio calcuation is based on cross product method unless ’fisher.or’ is set as
TRUE. It’s confidence limits are obtained by the exact method unless exact.ci.or is set as FALSE.
’cs’ and ’csi’ are for cohort and cross-sectional studies. It computes the absolute risk, risk difference, and risk ratio. When the exposure is a risk factor, the attributable fraction exposure, attributable fraction population and number needed to harm (NNH) are also displayed in the output.
When the exposure is a protective factor, protective efficacy or percent of risk reduced and number
needed to treat (NNT) are displayed instead.
If there are more than 2 exposure categories and the sample size is large enough, a graph will be
plotted.
’method’ in ’csi’ and ’cs’ chooses whether confidence limits of the risk difference should be computed by Newcomb-Wilson method. Both this and the standard method may give non-sensible
values if the risk difference is not statistically significant.
’make2x2’ creates a 2-by-2 table using the above orientation.
’graph.casecontrol’ and ’graph.prospective’ draw a graph comparing the odds of exposure between
cases and controls or odds of diseased between exposed and non-exposed.
These two graphic commands are automatically chosen by ’cc’ and ’cci’, depending on the ’design’
argument.
Alternatively, a contingency table saved from ’make2x2’ can be supplied as the ’cctable’ argument
for the ’cc’ function and so on.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’fisher.test’, ’chisq.test’ and ’mhor’
18
CI
Examples
data(Oswego)
.data <- Oswego
attach(.data)
cc(ill, chocolate)
cc(ill, chocolate, design="case-control")
cs(ill, chocolate) # The outcome variable should come first.
#
For the following table
#
chocolate
#
ill
FALSE TRUE
#
FALSE
7
22
#
TRUE
20
25
#
cci(25, 22, 20, 7)
graph.casecontrol(25, 22, 20, 7)
graph.prospective(25, 22, 20, 7)
# Each of the above two lines produces untitled graph, which can be decorated
# additionally decorated
#Alternatively
table1 <- make2x2(25,22,20,7)
cc(outcome=NULL, exposure=NULL, cctable=table1)
cs(outcome=NULL, exposure=NULL, cctable=table1)
agegr <- pyramid(age, sex, bin=30)$ageGroup
cs(ill, agegr, main="Risk ratio by age group", xlab="Age (years)")
rm(list=ls())
detach(.data)
CI
Confidence interval of probabilty, mean and incidence
Description
Compute confidence interval(s) of variables or values input from keyboard.
Usage
ci(x, ...)
## Default S3 method:
ci(x,...)
## S3 method for class 'binomial'
ci(x, size, precision, alpha = 0.05, ...)
## S3 method for class 'numeric'
ci(x, n, sds, alpha = 0.05, ...)
CI
19
## S3 method for class 'poisson'
ci(x, person.time, precision, alpha = 0.05, ...)
Arguments
x
a variable for ’ci’, number of success for ’ci.binomial’, mean(s) for ’ci.numeric’,
and counts for ’ci.poisson’
size
denominator for success
precision
level of precision used during computation for the confidence limits
alpha
significance level
n
sample size
sds
standard deviation
person.time
denominator for count
...
further arguments passed to or used by other methods
Details
These functions compute confidence intervals of probability, mean and incidence from variables in
a dataset or values from keyboard input.
’ci’ will try to identify the nature of the variable ’x’ and determine the appropriate method (between
’ci.binomial’ and ’ci.numeric’) for computation. ’ci’ without a specified method will never call
’ci.poisson’.
The specific method, ie. ’ci.binomial’, ’ci.numeric’ or ’ci.poisson’, should be used when the values
are input from the keyboard or from an aggregated data frame with columns of variables for the
arguments.
’ci.binomial’ and ’ci.numeric’ employ exact probability computation while ’ci.numeric’ is based on
the t-distribution assumption.
Value
’ci.binomial’ and ’ci.poisson’ return a data frame containing the number of events, the denominator
and the incidence rate. ’ci.numeric’ returns means and standard deviations. All of these are followed
by the standard error and the confidence limit, the level of which is determined by ’alpha’
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’summ’
20
CI
Examples
data(Oswego)
.data <- Oswego
attach(.data)
# logical variable
ci(ill)
# numeric variable
ci(age)
# factor
ci(sex=="M")
ci(sex=="F")
detach(.data)
# Example of confidence interval for means
library(MASS)
.data <- Cars93
attach(.data)
car.price <- aggregate(Price, by=list(type=Type), FUN=c("mean","length","sd"))
car.price
ci.numeric(x=car.price$mean, n=car.price$length, sds=car.price$sd.Price )
detach(.data)
rm(list=ls())
# Example of confidence interval for probabilty
data(ANCdata)
.data <- ANCdata
attach(.data)
death1 <- death=="yes"
death.by.group <- aggregate.numeric(death1,
by=list(anc=anc, clinic=clinic), FUN=c("sum","length"))
death.by.group
ci.binomial(death.by.group$sum.death1, death.by.group$length)
detach(.data)
rm(list=ls())
# Example of confidence interval for incidence
data(Montana)
.data <- Montana
attach(.data)
des(.data)
age.Montana <- aggregate.data.frame(Montana[,1:2],
by=list(agegr=Montana$agegr),FUN="sum")
age.Montana
ci.poisson(age.Montana$respdeath, person.time=age.Montana$personyrs)
detach(.data)
rm(list=ls())
# Keyboard input
# What is the 95 % CI of sensitivity of a test that gives all
# positive results among 40 diseased individuals
ci.binomial(40,40)
Codebook
21
# What is the 99 % CI of incidence of a disease if the number
# of cases is 25 among 340,000 person-years
ci.poisson(25, 340000, alpha=.01) # 4.1 to 12.0 per 100,000 person-years
Codebook
Codebook of a data frame
Description
Print description, summary statistics and one-way tabulation of variables
Usage
codebook(dataFrame)
Arguments
dataFrame
A data frame for printing the codebook
Details
The default value of dataFrame (ie if no argument is supplied) is ’.data’.
While ’summ’ produces summary statistics of both numeric and factor variables, ’codebook’ gives
summary statistics of all numeric variables and one-way tabulation of all factors of the data frame.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’use’, ’summ’, ’tab1’ and ’tableStack’
Examples
data(Familydata)
codebook(Familydata)
22
Data for cleaning
Data for cleaning
Dataset for practicing cleaning, labelling and recoding
Description
The data come from clients of a family planning clinic.
For all variables except id: 9, 99, 99.9, 888, 999 represent missing values
Usage
data(Planning)
Format
A data frame with 251 observations on the following 11 variables.
ID a numeric vector: ID code
AGE a numeric vector
RELIG a numeric vector: Religion
1
2
= Buddhist
= Muslim
PED a numeric vector: Patient’s education level
1
2
3
4
5
6
7
= none
= primary school
= secondary school
= high school
= vocational school
= university
= other
INCOME a numeric vector: Monthly income in Thai Baht
1
2
3
4
5
= nil
= < 1,000
= 1,000-4,999
= 5,000-9,999
= 10,000
AM a numeric vector: Age at marriage
REASON a numeric vector: Reason for family planning
des
23
1
2
3
= birth spacing
= enough children
= other
BPS a numeric vector: systolic blood pressure
BPD a numeric vector: diastolic blood pressure
WT a numeric vector: weight (Kg)
HT a numeric vector: height (cm)
Examples
data(Planning)
des(Planning)
# Change var. name to lowercase
names(Planning) <- tolower(names(Planning))
.data <- Planning
des(.data)
# Check for duplication of 'id'
attach(.data)
any(duplicated(id))
duplicated(id)
id[duplicated(id)] #215
# Which one(s) are missing?
setdiff(min(id):max(id), id) # 216
# Correct the wrong on
id[duplicated(id)] <- 216
detach(.data)
rm(list=ls())
des
Desription of a data frame or a variable
Description
Description of a data frame or a variable or wildcard for variable names
Usage
des(dataFrame)
Arguments
dataFrame
a data frame
24
DHF99
Details
The variable names will be listed with class and the description of each variable
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’summ’, ’label.var’, ’subset’ and ’keepData’
Examples
data(Oswego)
.data <- Oswego
des(.data)
DHF99
Dataset for exercise on predictors for mosquito larva infestation
Description
Dataset from a community survey on water containers infested by mosquito larvae.
Usage
data(DHF99)
Format
A data frame with 300 observations on the following 5 variables.
houseid a numeric vector
village a numeric vector indicating village ID
education a factor with levels Primary Secondary High school Bachelor Other
containers a numeric vector indicating number of containers infested
viltype a factor with levels rural urban slum
References
Thammapalo, S., Chongsuwiwatwong, V., Geater, A., Lim, A., Choomalee, K. 2005. Sociodemographic and environmental factors associated with Aedes breeding places in Phuket, Thailand.
Southeast Asian J Trop Med Pub Hlth 36(2): 426-33.
dotplot
25
dotplot
Dot plot
Description
Plot of frequency in dots
Usage
dotplot (x, bin = "auto", by = NULL, xmin = NULL, xmax = NULL,
time.format = NULL, time.step = NULL, pch = 18, dot.col = "auto",
main = "auto", ylab = "auto", cex.X.axis = 1, cex.Y.axis = 1, ...)
Arguments
x
a numeric vector. Allowed types also include "Date" and "POSIXct"
bin
number of bins for the range of ’x’
by
stratification variable
xmin
lower bound of x in the graph
xmax
upper bound of x in the graph
time.format
format for time or date at the tick marks
time.step
a character string indicating increment of the sequence of tick marks
pch
either an integer specifying a symbol or a single character to be used as the
default in plotting points
dot.col
a character or a numeric vector indicating the colour of each category of ’by’
main
main title
ylab
Y axis title
cex.X.axis
character extension scale of X axis
cex.Y.axis
character extension scale of Y axis
...
graphical parameters for the dots when there is no stratification
Details
’dotplot’ in Epicalc is similar to a histogram. Each dot represents one record. Attributes of the
dots can be further specified in ’...’ when there is no strafication. Otherwise, the dots are plotted
as a diamond shape and the colours are automatically chosen based on the current palette and the
number of strata.
When ’bin="auto"’ (by default), and the class of the vector is ’integer’, ’bin’ will be automatically
set to max(x)-min(x)+1. This strategy is also applied to all other time and date variables. Users can
try other values if the defaults are not to their liking. See the example of ’timeExposed’ below.
The argument ’xmin’ and ’xmax’ indicate the range of x to be displayed on the graph. These two
arguments are independent from the value of ’bin’, which controls only the number of columns for
the original data range.
26
dotplot
Dotplot usually starts the first tick mark on the X-axis at ’xmin’ (or min(x) if the ’xmin’ is not
specified). The argument ’time.step’ is typically a character string, containing one of ’sec’, ’min’,
’hour’, ’day’, ’DSTday’, ’week’, ’month’ or ’year’. This can optionally be preceded by an integer
and a space, or followed by "s", such as "2 weeks".
Setting proper ’xmin’, ’xmax’ and ’time.step’ can improve the location of tick marks on the Xaxis. The ’time.format’ argument can then be given to further improve the graph. See the last two
examples for a better understanding.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’summ’, ’hist’, ’seq.Date’ and ’seq.POSIXt’
Examples
a <- rep(1:2, 250)
b <- rnorm(500,mean=a)
dotplot(b)
dotplot(b, pch=1)
dotplot(b, by=a)
dotplot(b, by=a, pch=1) # You may try other values of 'pch'
#
#
#
#
For the commands below,
if dates in X axis are not readable,
try omitting '#' from the next line
Sys.setlocale("LC_ALL", "C")
# The number of dots in each column is the frequency
# of 'x' for the exact value on the X axis.
data(Outbreak)
.data <- Outbreak
attach(.data)
class(age) # numeric
dotplot(age) # 40 columns
age.as.integer <- as.integer(age)
dotplot(age.as.integer)
# 'bin' is the number of columns in the data range.
# Specifying 'min' and 'max' only expands or truncates
# the range of the X axis and has no effect on the distribution
# of the dots inside the data range.
dotplot(age.as.integer, xmin=0, xmax=150) # Just for demonstration.
dotplot(age.as.integer, xmin=0, xmax=70) # the "99"s are now out of the plot.
dotplot(age.as.integer, xmin=0, xmax=70, by=sex)
# Controlling colours of the dots
dotplot(age.as.integer, xmin=0, xmax=70, dot.col="chocolate")
sex1 <- factor(sex); levels(sex1) <- list("M"=1,"F"=0)
dotplot(age.as.integer, xmin=0, xmax=70, by=sex1, dot.col=c(2,5))
dotplot(age.as.integer, xmin=0, xmax=70, by=sex1,
Ectopic pregnancy
27
dot.col=c("brown","blue"), main="Age by sex",
cex.X.axis=1.3, cex.Y.axis=1.5, cex.main=1.5)
rm(list=ls())
detach(.data)
Ectopic pregnancy
Dataset of a case-control study looking at history of abortion as a risk
factor for ectopic pregnancy
Description
This case-control study has one case series and two control groups.
The subjects were recruited based on three types of pregnancy outcome
Usage
data(Ectopic)
Format
A data frame with 723 observations on the following 4 variables.
id a numeric vector
outc a factor with levels EP IA Deli
EP
IA
Deli
= ectopic pregnancy
= women coming for induced abortion
= women admitted for full-term delivery
hia a factor with levels never IA ever IA
gravi a factor with levels 1-2 3-4 >4
Examples
data(Ectopic)
library(nnet)
data(Ectopic)
.data <- Ectopic
multi1 <- multinom(outc ~ hia + gravi, data=.data)
summary(multi1)
mlogit.display(multi1)
# Changing referent group of outcome
.data$outcIA <- relevel(.data$outc, ref="IA")
multi2 <- multinom(outcIA ~ hia + gravi, data=.data)
summary(multi2)
mlogit.display(multi2)
28
Familydata
Familydata
Dataset of a hypothetical family
Description
Anthropometric and financial data of a hypothetical family
Usage
data(Familydata)
Format
A data frame with 11 observations on the following 6 variables.
code a character vector
age a numeric vector
ht a numeric vector
wt a numeric vector
money a numeric vector
sex a factor with levels F M
Examples
data(Familydata)
.data <- Familydata
des(.data)
summ(.data)
age2 <- with(.data, age)^2
with(.data, plot(age, money, log="y"))
dots.of.age <- seq(0,80,0.01)
new.data.frame <- data.frame(age=dots.of.age, age2=dots.of.age^2)
lm1 <- lm(log(money) ~ age + age2, data=.data)
summary(lm1)$coefficients
dots.of.money <- predict.lm(lm1, new.data.frame)
lines(dots.of.age, exp(dots.of.money), col="blue")
Follow-up Plot
Follow-up Plot
29
Longitudinal followup plot
Description
Plot longitudinal values of individuals with or without stratification
Usage
followup.plot(id, time, outcome, by = NULL, n.of.lines = NULL, legend = TRUE,
legend.site = "topright", lty = "auto", line.col = "auto",
stress = NULL, stress.labels = FALSE, label.col = 1, stress.col = NULL,
stress.width = NULL, stress.type = NULL, lwd = 1, xlab, ylab, ...)
Arguments
id
idenfication variable of the same subject being followed up
time
time at each measurement
outcome
continuous outcome variable
by
stratification factor (if any)
n.of.lines
number of lines (or number of subjects in the data frame) randomly chosen for
drawing
legend
whether a legend will be automatically included in the graph
legend.site
a single character string indicating location of the legend. See details of ?legend
lty
type of the "time" lines. See ’lty’ in ?par
line.col
line colour(s) for non-stratified plot
stress
subset of ids to draw stressed lines
stress.labels
whether the stressed lines should be labelled
label.col
single integer indicating colour of the stressed line labels
stress.col
colour values used for the stressed line. Default value is ’1’ or black
stress.width
relative width of the stressed line
stress.type
line type code for the stressed line
lwd
line width
xlab
label for X axis
ylab
label for Y axis
...
other graphic parameters
30
Follow-up Plot
Details
’followup.plot’ plots outcome over time of the individual subjects.
If a stratification variable ’by’ is specified, the levels of this variable will be used to color the lines.
’n.of.lines’ is used to reduce the number of lines to allow the pattern to be seen more clearly.
’legend’ is omitted if ’n.of.lines’ is not NULL or the number of subjects exceeds 7 without stratification.
’line.col’ works only for a non-stratified plot. It can be a single standard colour or "multicolor".
Values for ’stress.col’, ’stress.width’ and ’stress.type’, if not NULL, should follow those for ’col’,
’lwd’ and ’lty’, respectively
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’plot’,’lines’
Examples
.data <- Indometh
attach(.data)
followup.plot(Subject, time, conc)
followup.plot(Subject, time, conc, lty=1:6, line.col=rep("black",6))
detach(.data)
.data <- Sitka
attach(.data)
followup.plot(tree,
followup.plot(tree,
followup.plot(tree,
followup.plot(tree,
Time,
Time,
Time,
Time,
size)
size, line.col = "brown")
size, line.col = "multicolor")
size, n.of.lines=20, line.col = "multicolor")
# Breakdown of color by treatment group
followup.plot(tree, Time, size, by=treat)
# The number of lines reduced to 40
followup.plot(tree, Time, size, by=treat, n.of.lines=40)
# Stress some lines
length(table(tree)) # 79 trees followed up
# Identifying trees that sometimes became smaller
.data <- .data[order(.data$tree, .data$Time),]
detach(.data)
attach(.data)
next.tree <- c(tree[-1], NA)
next.size <- c(size[-1], NA)
next.size[tree != next.tree] <- NA
Hakimi’s data
31
smaller.trees <- tree[next.size < size]
followup.plot (tree, Time, size, line.col=5, stress=smaller.trees,
stress.col=2, stress.width=2, stress.type=2)
followup.plot (tree, Time, size, line.col=5, stress=smaller.trees,
stress.col=2, stress.width=2, stress.type=2, stress.labels=TRUE)
detach(.data)
rm(list=ls())
Hakimi’s data
Dataset on effect of training personnel on neonatal mortality
Description
Subset of a dataset from an intervention trial of education on personnel and the effect on neonatal
mortality. Non-fatal records were randomly selected from the original dataset, just for practice and
interpretation of interaction term.
Usage
data(Hakimi)
Format
A data frame containing 456 observations and 4 variables.
dead neonatal death: 1=yes, 0=no
treatment intervention programme: 1=yes, 2=no
malpres malpresentation of fetus: 1=yes, 0=no
birthwt birth weight for foetus in gram
Examples
data(Hakimi)
.data <- Hakimi
attach(.data)
cc(dead, treatment)
mhor(dead, treatment, malpres)
detach(.data)
32
Hookworm and blood loss
Hookworm 1993
Dataset from a study on hookworm prevalence and intensity in 1993
Description
A dataset from a cross-sectional survey in 1993 examining hookworm infection
Usage
data(HW93)
Format
A data frame with 637 observations on the following 6 variables.
id a numeric vector for personal identification number
epg a numeric vector for eggs per gram of faeces
age a numeric vector for age in years
shoe a factor for shoe wearing with levels no yes
intense a factor for intensity of infection in epg. with levels 0 1-1,999 2,000+
agegr a factor for age group with levels <15 yrs 15-59 yrs 60+ yrs
Examples
data(HW93)
des(HW93)
.data <- HW93
.data$order.intense <- ordered(.data$intense)
ord.hw <- polr(ordered(intense) ~ agegr + shoe, data=.data)
summary(ord.hw)
ordinal.or.display(ord.hw)
Hookworm and blood loss
Hookworm infection and blood loss: SEAJTM 1970
Description
A study using radio-isotope to examine daily blood loss and number of hookworms infecting the
patients.
Usage
data(Suwit)
IUD trial admission data
33
Format
A data frame with 15 observations on the following 3 variables.
id a numeric vector
worm a numeric vector: number of worms
bloss a numeric vector: estimated daily blood loss (mg/day)
Source
Areekul, S., Devakul, K., Viravan, C., Harinasuta, C. 1970 Studies on blood loss, iron absorption
and iron reabsorption in hookworm patients in Thailand. Southeast Asian J Trop Med Pub Hlth
1(4): 519-523.
References
~~ possibly secondary sources and usages ~~
Examples
data(Suwit)
with(Suwit, plot(worm, bloss, type="n"))
with(Suwit, text(worm, bloss, labels=id))
abline(lm(bloss ~ worm, data=Suwit), col="red")
IUD trial admission data
Dataset admission of cases for IUD trials
Description
This dataset is a subset of WHO IUD trial. It should be merged with IudFollowup and IudDiscontinue
Usage
data(IudAdmit)
Format
A data frame containing 918 observations and 4 variables.
id a numeric vector for personal identification number
idate date of IUD insertion
lmptime time since last menstrual period
a122 type of IUD
Examples
data(IudAdmit)
34
IUD trial follow-up data
IUD trial discontinuation data
Dataset on discontinuation of the IUD trial cases
Description
This dataset is a subset of WHO IUD trial. It should be merged with IudAdmit and IudFollowup
Usage
data(IudDiscontinue)
Format
A data frame containing 398 observations and 3 variables.
id a numeric vector for personal identification number
discdate date of discontinuation
d23 primary reason for discontinuation
Examples
data(IudDiscontinue)
IUD trial follow-up data
Dataset followup cases of IUD trials
Description
This dataset is a subset of WHO IUD trial. It should be merged with IudAdmit and IudDiscontinue
Usage
data(IudFollowup)
Format
A data frame containing 4235 observations and 6 variables.
id a numeric vector for personal identification number
vlmpdate date of last mentrual period before this visit
vdate date of visit
f22 lactating
f51 IUD threads visible
f61 subject continuing
kap
35
Examples
data(IudFollowup)
kap
Kappa statistic
Description
Measurement of agreement in categorization by 2 or more raters
Usage
kap(x, ...)
## Default S3 method:
kap(x, ...)
## S3 method for class 'table'
kap(x, decimal =3, wttable = c(NULL, "w", "w2"), print.wttable = FALSE, ...)
## S3 method for class '2.raters'
kap(x, rater2, decimal =3, ...)
## S3 method for class 'm.raters'
kap(x, decimal =3, ...)
## S3 method for class 'ByCategory'
kap(x, decimal =3, ...)
Arguments
x
an object serving the first argument for different methods
FUNCTION
’kap.table’
’kap.2.raters’
’kap.m.raters’
’kap.ByCategory’
decimal
wttable
print.wttable
rater2
...
’x’
table
rater1
data frame with raters in column
data frame with categories in column
number of decimal in the print
cross tabulation of weights of agreement among categories. Applicable only for
’kap.table’ and ’kap.2.raters’
whether the weights table will be printed out
a vector or factor containing opinions of the second rater among two raters.
further arguments passed to or used by other methods.
36
kap
Details
There are two different principles for the calculation of the kappa statistic. ’kap.table’ and ’kap.2.raters’
use two fixed raters whereas ’kap.m.raters’ and ’kap.ByCategory’ are based on frequency of category of rating an individual received without a requirement that the raters must be fixed.
’kap.table’ analyses kappa statistics from a predefined table of agreement of two raters.
’wttable’ is important only if the rating can be more than 2 levels. If this argument is left as default
or ’NULL’, full agreement will be weighted as 1. Partial agreement is considered as non-agreement
and weighted as 0.
When ’wttable = "w"’ the weights are given by
1 − abs(i − j)/(1 − k)
where i and j index the rows and columns of the ratings and k is the maximum number of possible
ratings. A weight of 1 indicates an observation of perfect agreement.
When ’wttable = "w2", the weights are given by
1 − (abs(i − j)/(1 − k))2 .
In this case, weights of partial agreements will further increase.
’wttable’ can otherwise be defined by the user.
’kap.2.raters’ takes two vectors or factors, one for each of the two raters. Cross-tabulation of the two
raters is displayed and automatically forwarded for computation of kappa statistic by ’kap.table’.
’kap.m.raters’ is used for more than 2 raters. Although the variables are arranged based on columns
of individual raters, only the frequency in each category rating is used. This function calculates
the frequencies without any display and automatically forwards the results for computation by
’kap.ByCategory’.
’kap.ByCategory’ is for the grouped data format, where each category (column) contains the counts
for each individual subject being rated. As mentioned above, the frequencies can come from different sets of raters.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’table’
Examples
## Computation of kappa from a table
class <- c("Normal","Benign","Suspect","Cancer")
raterA <- gl(4,4, label=class)
raterB <- gl(4,1,16, label=class)
freq
<- c(50,2,0,1,2,30,4,3,0,0,20,1,1,3,4,25)
table1 <- xtabs(freq ~ raterA + raterB)
table1
kap(table1)
List non-function objects
wt <-c(1,.5,0,0,.5,1,0,0,0,0,1,.8,0,0,.8,1)
wttable <- xtabs(wt ~ raterA + raterB)
wttable # Agreement between benign vs normal is .5, suspect vs cancer is .8
kap(table1, wttable=wttable, print.wttable=TRUE)
# The following two lines are computational possible but inappropriate
kap(table1, wttable = "w", print.wttable=TRUE)
kap(table1, wttable = "w2", print.wttable=TRUE)
## A data set from 5 raters with 3 possible categories.
category.lab <- c("yes","no","Don't know")
rater1 <- factor(c(1,1,3,1,1,1,1,2,1,1), labels=category.lab)
rater2 <- factor(c(2,1,3,1,1,2,1,2,3,1), labels=category.lab)
rater3 <- factor(c(2,3,3,1,1,2,1,2,3,1), labels=category.lab)
rater4 <- factor(c(2,3,3,1,3,2,1,2,3,3), labels=category.lab)
rater5 <- factor(c(2,3,3,3,3,2,1,3,3,3), labels=category.lab)
kap.m.raters(data.frame(rater1,rater2,rater3,rater4,rater5))
# The above is the same as
YES <- c(1,2,0,4,3,1,5,0,1,3)
NO <- c(4,0,0,0,0,4,0,4,0,0)
DONTKNOW <- c(0,3,5,1,2,0,0,1,4,2)
kap.ByCategory(data.frame(YES,NO,DONTKNOW))
# Using 'kap.m.raters' for 2 raters is inappropriate. Kappa obtained
# from this method assumes that the agreement can come from any two raters,
# which is usually not the case.
kap.m.raters(data.frame(rater1, rater2))
# 'kap.2.raters' gives correct results
kap.2.raters(rater1, rater2)
# When there are missing values,
rater3[9] <- NA; rater4[c(1,9)] <- NA
kap.m.raters(data.frame(rater1,rater2,rater3,rater4,rater5))
# standard errors and other related statistics are not available.
# Two exclusive rating categories give only one common set of results.
# The standard error is obtainable even if the numbers of raters vary
# among individual subjects being rated.
totalRaters <- c(2,2,3,4,3,4,3,5,2,4,5,3,4,4,2,2,3,2,4,5,3,4,3,3,2)
pos <- c(2,0,2,3,3,1,0,0,0,4,5,3,4,3,0,2,1,1,1,4,2,0,0,3,2)
neg <- totalRaters - pos
kap.ByCategory(data.frame(neg, pos))
List non-function objects
List non-function objects
Description
List all objects visible in the global environment except user created functions.
37
38
lookup
Usage
lsNoFunction()
Details
Compared to standard ’ls()’, this function displays only the subset of ’ls()’ which are not functions.
The member of this list can be removed by ’zap()’ but not the set of the functions created.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’use’, ’detach’, ’ls’, ’rm’
Examples
object1 <- 1:5
object2 <- list(a=3, b=5)
function1 <- function(x) {x^3 +1}
ls()
lsNoFunction()
## To show only functions
as.character(lsf.str()[])
lookup
Recode several values of a variable
Description
Systematic replacement of several values of a variable using an array
Usage
lookup(x, lookup.array)
Arguments
x
a variable
lookup.array
a n-by-2 array used for looking up the recoding scheme
lrtest
39
Details
This command is used for changing more than one value of a variable using a n-by-2 look-up array.
The first column of the look-up array (index column) must be unique.
If either the variable or the look-up table is character, the result vector will be character.
For changing the levels of a factor variable, ’recode(vars, "old level", "new level")’ or ’levels(var)
<- ’ instead.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’replace’, ’recode’
Examples
a
tx
<- c( 1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5, NA)
<- rbind(c(1,2),c(2,1),c(3,4),c(4,NA),c(NA,3))
# Swapping values of 1 and 2; rotating 3, 4 and NA
new.a <- lookup(a, tx)
data.frame(a, new.a)
tableA <- table(a, new.a, exclude=NULL)
# All non-diagonal cells which are non-zero are the recoded cells.
print(tableA, zero=".")
## Character look-up table
b <- c(rep(letters[1:4],2), ".", NA)
tx1 <- cbind(c(letters[1:5], ".", NA), c("Disease A","Disease B","Disease C",
"Disease D","Disease E", NA, "Unknown"))
DiseaseName <- lookup(b, tx1)
data.frame(b, DiseaseName)
lrtest
Likelihood ratio test
Description
Likelihood ratio test for objects of class ’glm’
Usage
lrtest (model1, model2)
Arguments
model1, model2 Two models of class "glm" having the same set of records and the same type
(’family’ and ’link’)
40
Matched case-control study
Details
Likelihood ratio test checks the difference between -2*logLikelihood of the two models against the
change in degrees of freedom using a chi-squared test. It is best applied to a model from ’glm’
to test the effect of a factor with more than two levels. The records used in the dataset for both
models MUST be the same. The function can also be used with "clogit", which does not have real
logLikelihood.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’glm’, ’logLik’, ’deviance’
Examples
model0
model1
lrtest
lrtest
lrtest
a
<- glm(case ~ induced + spontaneous, family=binomial, data=infert)
<- glm(case ~ induced, family=binomial, data=infert)
(model0, model1)
(model1, model0) # same result
(model1, model0) -> a
Matched case-control study
Datasets on a matched case-control study of esophageal cancer
Description
Two different datasets for the same matched case-control study. VC1to6 has 1 case : varying
number of controls (from 1 to 6) whereas VC1to1 has the number of control reduced to 1 for each
case.
Usage
data(VC1to1)
data(VC1to6)
Format
A data frame with the following 5 variables.
matset a numeric vector indicating matched set number from 1 to 26
case a numeric vector: 1=case, 0=control
smoking a numeric vector: 1=smoker, 0=non-smoker
rubber a numeric vector: 1=exposed, 0=never exposed to rubber industry
alcohol a numeric vector: 1=drinker, 0=non-drinker
matchTab
41
Source
Chongsuvivatwong, V. 1990 A case-control study of esophageal cancer in Southern Thailand. J
Gastro Hep 5:391–394.
See Also
’infert’ in the datasets package.
Examples
data(VC1to6)
.data <- VC1to6
des(.data)
with(.data, matchTab(case, alcohol, matset))
rm(.data)
matchTab
Matched tabulation
Description
Tabulation of outcome vs exposure from a matched case control study
Usage
matchTab (case, exposed, strata, decimal)
Arguments
case
Outcome variables where 0 = control and 1 = case
exposed
Exposure variable where 0 = non-exposed and 1 = exposed
strata
Identification number for each matched set
decimal
Number of digits displayed after the decimal point
Details
Tabulation for an unmatched case control study is based on individual records classified by outcome
and exposure variables.
Matched tabulation is tallying based on each matched set. The simplest form is McNemar’s table
where only one case is matched with one control. ’matchTab’ can handle 1:m matching where m
can vary from 1 to m. A MLE method is then used to compute the conditional odds ratio.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
42
mhor
See Also
’table’, ’cc’ and ’clogit’
Examples
.data <- infert
## Not run:
# matchTab(case, induced, stratum)
# Tabulation successful but OR not computed
# because 'induced' is not binary
## End(Not run)
attach(.data)
ia <- induced > 0 # any induced abortion
matchTab(case, ia, stratum)
# See also
clogit(case ~ ia + strata(stratum), data=infert)
detach(.data)
rm(list=ls())
mhor
Mantel-Haenszel odds ratio
Description
Mantel-Haenszel odds ratio calculation and graphing from a stratified case-control study
Usage
mhor(..., mhtable = NULL, decimal=2, graph = TRUE, design = "cohort")
Arguments
...
Three variables viz. ’outcome’, ’exposure’ and ’stratification’.
mhtable
a 2-by-2-by-s table, where s (strata) is more than one
decimal
number of decimal places displayed
graph
If TRUE (default), produces an odds ratio plot
design
Specification for graph; can be "case control","case-control", "cohort" or "prospective"
Details
’mhor’ computes stratum-specific odds ratios and 95 percent confidence intervals and the MantelHaenszel odds ratio and chi-squared test is given as well as the homogeneity test. A stratified odds
ratio graph is displayed.
Montana
43
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’fisher.test’, ’chisq.test’
Examples
data(Oswego)
with(Oswego, cc(ill, chocolate))
with(Oswego, mhor(ill, chocolate, sex))
mht1 <- with(Oswego, table(ill, chocolate, sex))
dim(mht1)
mhor(mhtable=mht1) # same results
Montana
Dataset on arsenic exposure and respiratory deaths
Description
Dataset from a cohort study of exposure to arsenic from industry and deaths from respiratory diseases.
Usage
data(Montana)
Format
A data frame with 114 observations on the following 6 variables.
respdeath a numeric vector indicating number of deaths from respiratory diseases
personyrs a numeric vector indicating person-years of exposure
agegr a numeric vector: 1=40-49, 2=50-59, 3=60-69, 4=70-79)
period a numeric vector: 1=1938-1949, 2=1950-1959, 3=1960-1969, 4=1970-1977
starting a numeric vector indicating starting period: 1=pre-1925, 2=1925 & after
arsenic a numeric vector indicating years of exposure: 1=<1 year, 2=1-4 years, 3=5-14 years,
4=15+ years
44
Outbreak investigation
Oswego
Dataset from an outbreak of food poisoning in US
Description
This dataset contains information on the records of 75 persons under investigation for the cause of
acute food poisoning after a dinner party.
Usage
data(Oswego)
Format
A data frame containing 75 observations and 20 variables.
Source
EpiInfo package
Examples
data(Oswego)
.data <- Oswego
attach(.data)
pyramid(age, sex)
detach(.data)
Outbreak investigation
Dataset from an outbreak of food poisoning on a sportsday, Thailand
1990.
Description
This dataset contains information from an outbreak investigation concerning food poisoning on a
sportsday in Thailand 1990.
Dichotomous variables for exposures and symptoms were coded as follow:
0
1
9
= no
= yes
= missing or unknown
Outbreak investigation
45
Usage
data(Outbreak)
Format
A data frame with 1094 observations on the following 13 variables.
id a numeric vector
sex a numeric vector
0
1
= female
= male
99
= missing
age a numeric vector: age in years
exptime an AsIs or character vector of exposure times
beefcurry a numeric vector: whether the subject had eaten beefcurry
saltegg a numeric vector: whether the subject had eaten salted eggs
eclair a numeric vector: pieces of eclair eaten
80
90
= ate but could not remember how much
= totally missing information
water a numeric vector: whether the subject had drunk water
onset an AsIs or character vector of onset times
nausea a numeric vector
vomiting a numeric vector
abdpain a numeric vector: abdominal pain
diarrhea a numeric vector
References
Thaikruea, L., Pataraarechachai, J., Savanpunyalert, P., Naluponjiragul, U. 1995 An unusual outbreak of food poisoning. Southeast Asian J Trop Med Public Health 26(1):78-85.
Examples
data(Outbreak)
.data <- Outbreak
# Distribution of reported pieces of eclair taken
attach(.data)
46
poisgof
tab1(eclair)
# Defining missing value
.data$eclair[.data$eclair > 20] <- NA
detach(.data)
attach(.data)
pieces.of.eclair <- cut(eclair, c(0,1,2,20), include.lowest=TRUE, right=FALSE)
tabpct(pieces.of.eclair, diarrhea)
rm(list=ls())
detach(.data)
poisgof
Goodness of fit test for modeling of count data
Description
Poisson and negative binomial regression are used for modeling count data. This command tests
the deviance against the degrees of freedom in the model thus determining whether there is overdispersion.
Usage
poisgof(model)
Arguments
model
A Poisson or negative binomial model
Details
To test the significance of overdispersion of the errors of a Poisson or negative binomial model,
the deviance is tested against degrees of freedom using chi-squared distribution. A low P value
indicates significant overdispersion.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
‘glm’
Examples
library(MASS)
quine.pois <- glm(Days ~ Sex/(Age + Eth*Lrn), data = quine, family=poisson)
poisgof(quine.pois)
quine.nb1 <- glm.nb(Days ~ Sex/(Age + Eth*Lrn), data = quine)
poisgof(quine.nb1)
Power
47
Power
Power calculation for two sample means and proportions
Description
Calculation of power given the results from a study
Usage
power.for.2p(p1, p2, n1, n2, alpha = 0.05)
power.for.2means(mu1, mu2, n1, n2, sd1, sd2, alpha = 0.05)
Arguments
p1, p2
probabilities of the two samples
n1, n2
sample sizes of the two samples
alpha
significance level
mu1, mu2
means of the two samples
sd1, sd2
standard deviations of the two samples
Details
These two functions compute the power of a study from the given arguments
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’n.for.2means’, ’n.for.2p’
Examples
# Suppose, in the example found in 'help(n.for.2p)',
# given the two proportions are .8 and .6 and the sample size
# for each group is 60.
power.for.2p(p1=.8, p2=.6, n1=60, n2=60) # 59 percent
#
#
#
#
If the means of a continuous outcome variable in the same
two groups were 50 and 60 units and the standard deviations were 30
and 35 units, then the power to detect a statistical significance
would be
power.for.2means(mu1=50, mu2=60, sd1=30, sd2=35, n1=60, n2=60)
# 39 percent. Note the graphic display
48
print cci
print alpha
Print alpha object
Description
Print results related to Cronbach’s alpha
Usage
## S3 method for class 'alpha'
print(x, ...)
Arguments
x
object of class ’alpha’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’tableStack’
Examples
data(Attitudes)
alpha(qa1:qa18, dataFrame=Attitudes) -> a
print(a)
a
print cci
Print cci results
Description
Print results for cci and cc commands
Usage
## S3 method for class 'cci'
print(x, ...)
print des
49
Arguments
x
object of class ’cci’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’cci’
print des
Print ’des’ results
Description
Print description of data frame of a variable
Usage
## S3 method for class 'des'
print(x, ...)
Arguments
x
object of class ’des’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’des’
50
print kap.table
print kap.ByCategory
Print kap.ByCategory results
Description
Print results for kap.Bycategory commands
Usage
## S3 method for class 'kap.ByCategory'
print(x, ...)
Arguments
x
object of class ’kap.ByCategory’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’kap.ByCategory’
print kap.table
Print kap.table results
Description
Print results for kap.table commands
Usage
## S3 method for class 'kap.table'
print(x, ...)
Arguments
x
object of class ’kap.table’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
print lrtest
51
See Also
’kap.table’
print lrtest
Print lrtest results
Description
Print results for likelihood ratio test
Usage
## S3 method for class 'lrtest'
print(x, ...)
Arguments
x
object of class ’lrtest’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’logistic.display’
Examples
model0
model1
lrtest
lrtest
a
<- glm(case ~ induced + spontaneous, family=binomial, data=infert)
<- glm(case ~ induced, family=binomial, data=infert)
(model0, model1)
(model1, model0) -> a
52
print n.for.2means
print matchTab
Print matched tabulation results
Description
Print matched tabulation results
Usage
## S3 method for class 'matchTab'
print(x, ...)
Arguments
x
object of class ’matchTab’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’matchTab’
print n.for.2means
Print n.for.2means results
Description
Print results for sample size for hypothesis testing of 2 means
Usage
## S3 method for class 'n.for.2means'
print(x, ...)
Arguments
x
object of class ’n.for.2means’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
print n.for.2p
53
See Also
’n.for.2p’
Examples
n.for.2means(mu1 = 10, mu2 = 14, sd1=3, sd2=3.5)
n.for.2means(mu1 = 10, mu2 = 7:14, sd1=3, sd2=3.5) -> a
a
print n.for.2p
Print n.for.2p results
Description
Print results for sample size for hypothesis testing of 2 proportions
Usage
## S3 method for class 'n.for.2p'
print(x, ...)
Arguments
x
object of class ’n.for.2p’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’n.for.2p’
Examples
n.for.2p(p1=.1, p2=.2)
n.for.2p(p1=seq(1,9,.5)/10, p2=.5)
54
print n.for.cluster.2p
print n.for.cluster.2means
Print n.for.cluster.2means results
Description
Print results for sample size for hypothesis testing of 2 means in cluster RCT
Usage
## S3 method for class 'n.for.cluster.2means'
print(x, ...)
Arguments
x
object of class ’n.for.cluster.2means’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’n.for.cluster.2means’
print n.for.cluster.2p
Print n.for.cluster.2p results
Description
Print results for sample size for hypothesis testing of 2 proportions in cluster RCT
Usage
## S3 method for class 'n.for.cluster.2p'
print(x, ...)
Arguments
x
object of class ’n.for.cluster.2p’
...
further arguments passed to or used by methods.
print n.for.equi.2p
55
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’n.for.cluster.2p’
print n.for.equi.2p
Print n.for.equi.2p results
Description
Print results for sample size for hypothesis testing of 2 proportions in equivalent trial
Usage
## S3 method for class 'n.for.equi.2p'
print(x, ...)
Arguments
x
object of class ’n.for.equi.2p’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’n.for.2p’
Examples
n.for.equi.2p(p=.85, sig.diff=.05)
56
print n.for.noninferior.2p
print n.for.lqas
Print n.for.lqas results
Description
Print results for sample size for lot quality assurance sampling
Usage
## S3 method for class 'n.for.lqas'
print(x, ...)
Arguments
x
object of class ’n.for.lqas’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
Examples
n.for.lqas(p0 = 0.05, q=0)
n.for.lqas(p0 = (10:1)/100, q=0 ) -> a
a
print n.for.noninferior.2p
Print n.for.noninferior.2p results
Description
Print results for sample size for hypothesis testing of 2 proportions in non-inferior trial
Usage
## S3 method for class 'n.for.noninferior.2p'
print(x, ...)
Arguments
x
object of class ’n.for.noninferior.2p’
...
further arguments passed to or used by methods.
print n.for.survey
57
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’n.for.2p’
Examples
n.for.noninferior.2p(p=.85, sig.inferior=.05)
print n.for.survey
Print n.for.survey results
Description
Print results for sample size of a continuous variable
Usage
## S3 method for class 'n.for.survey'
print(x, ...)
Arguments
x
object of class ’n.for.survey’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’n.for.2p’
Examples
n.for.survey(p=seq(5,95,5)/100)
58
print power.for.2p
print power.for.2means
Print power.for.2means results
Description
Print results for power for hypothesis testing of 2 means
Usage
## S3 method for class 'power.for.2means'
print(x, ...)
Arguments
x
object of class ’power.for.2means’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’n.for.2means’
Examples
power.for.2means(mu1 = 10, mu2=14,
n1=5, n2=7, sd1=3, sd2=3.5)
power.for.2means(mu1 = 10, mu2=7:14, n1=20, n2=25, sd1=3, sd2=3.5) -> a
a
print power.for.2p
Print power.for.2p results
Description
Print results for power of hypothesis testing of 2 proportions
Usage
## S3 method for class 'power.for.2p'
print(x, ...)
print statStack
59
Arguments
x
object of class ’power.for.2p’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’n.for.2p’
Examples
power.for.2p(p1=.1, p2=.2, n1=10, n2=15)
power.for.2p(p1=seq(1,9,.5)/10, p2=.5, n1=100, n2=120)
print statStack
Print statStack object
Description
Print a statStack object
Usage
## S3 method for class 'statStack'
print(x, ...)
Arguments
x
object of class ’statStack’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’statStack’
60
print summ.default
print summ.data.frame Print summary of the data frame
Description
Print summary of data frame
Usage
## S3 method for class 'summ.data.frame'
print(x, ...)
Arguments
x
object of class ’summ.data.frame’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’summ’
print summ.default
Print summary of a variable
Description
Print summary of a variable
Usage
## S3 method for class 'summ.default'
print(x, ...)
Arguments
x
object of class ’summ.default’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
print tableStack
61
See Also
’summ’
print tableStack
Print tableStack object
Description
Print a tableStack object
Usage
## S3 method for class 'tableStack'
print(x, ...)
Arguments
x
object of class ’tableStack’
...
further arguments passed to or used by methods.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’tableStack’
Examples
data(Attitudes)
tableStack(qa1:qa18, dataFrame=Attitudes) -> a
print(a)
data(Ectopic)
tableStack(hia, gravi, by=outc, dataFrame=Ectopic) -> b
print(b)
62
pyramid
pyramid
Population pyramid
Description
Create a population pyramid from age and sex
Usage
pyramid (age, sex, binwidth = 5, inputTable = NULL, printTable = FALSE,
percent = c("none", "each", "total"), col.gender = NULL,
bar.label = "auto", decimal = 1, col = NULL, cex.bar.value = 0.8,
cex.axis = 1, main = "auto", cex.main = 1.2, ...)
Arguments
age
a numeric variable for age
sex
a variable of two levels for sexes, can be numeric but preferrably factor with
labelled levels or characters
binwidth
bin width of age for each bar
inputTable
a table to read in with two columns of sexes and rows of age groups
printTable
whether the output table should be displayed on the console
percent
whether the lengths of the bars should be calculated from freqencies (default),
percentages of each sex or total percentages
col.gender
vector reflecting colours of the two gender
bar.label
whether the bars would be labelled with the values
decimal
number of decimals displayed in the percent output table
col
colour(s) of the bars
cex.bar.value
character extension factor of the bar labels
cex.axis
character extension factor of the axis
main
main title
cex.main
character extension factor of main title
...
graph options for the bars, e.g. col
Details
’pyramid’ draws a horizontal bar graph of age by sex.
The parameters of graph (par) options can be applied to ’font.lab’ and those of the bars, e.g. ’col’
but not of others.
Other lower level graph commands should be only for adding a ’title’.
’bar.label’ when set as "auto", will be TRUE when ’percent="each"’ or ’percent="total"’
Risk.display
63
Value
When the variables age and sex are input arguments, the return object includes age group variable
and the output table. The argument ’decimal’ controls only decimals of the output displayed on the
console but not the returned table.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’barplot’, ’levels’, ’table’
Examples
data(Oswego)
.data <- Oswego
attach(.data)
pyramid(age, sex)
pyramid(age, sex,
pyramid(age, sex,
pyramid(age, sex,
pyramid(age, sex,
pyramid(age, sex,
pyramid(age, sex,
bar.label = TRUE)
printTable=TRUE)
percent = "each", printTable=TRUE)
percent = "total", printTable=TRUE)
percent = "total", bar.label = FALSE)
percent = "total", cex.bar.value = .5)
pyramid(age, sex, col="red")
pyramid(age, sex, col=1:16) # Too colorful!
pyramid(age, sex, col.gender = c("pink","lightblue"))
output <- pyramid(age, sex, binwidth = 10, percent="each", decimal=2)
agegr <- output$ageGroup
detach(.data)
rm(list=ls())
# Drawing population pyramid from an exisiting table
pyramid(inputTable=VADeaths[,1:2], font.lab=4)
pyramid(inputTable=VADeaths[,1:2], font.lab=4, main=NULL)
title("Death rates per 100 in rural Virginia in 1940")
Risk.display
Tables for multivariate odds ratio, incidence density etc
Description
Display of various epidemiological modelling results in a medically understandable format
64
Risk.display
Usage
logistic.display(logistic.model, alpha = 0.05, crude = TRUE,
crude.p.value = FALSE, decimal = 2, simplified = FALSE)
clogistic.display(clogit.model, alpha = 0.05, crude=TRUE,
crude.p.value=FALSE, decimal = 2, simplified = FALSE)
cox.display (cox.model, alpha = 0.05, crude=TRUE, crude.p.value=FALSE,
decimal = 2, simplified = FALSE)
regress.display(regress.model, alpha = 0.05, crude = FALSE,
crude.p.value = FALSE, decimal = 2, simplified = FALSE)
idr.display(idr.model, alpha = 0.05, crude = TRUE, crude.p.value = FALSE,
decimal = 2, simplified = FALSE)
mlogit.display(multinom.model, decimal = 2, alpha = 0.05)
ordinal.or.display(ordinal.model, decimal = 3, alpha = 0.05)
tableGlm (model, modified.coeff.array, decimal)
## S3 method for class 'display'
print(x, ...)
Arguments
logistic.model a model from a logistic regression
clogit.model
a model from a conditional logistic regression
regress.model
a model from a linear regression
cox.model
a model from a cox regression
idr.model
a model from a Poisson regression or a negative binomial regression
multinom.model a model from a multinomial or polytomous regression
ordinal.model
a model from an ordinal logistic regression
alpha
significance level
crude
whether crude results and their confidence intervals should also be displayed
crude.p.value
whether crude P values should also be displayed if and only if ’crude=TRUE’
decimal
number of decimal places displayed
simplified
whether the display should be simplified
model
model passed from logistic.display or regress.display to tableGlm
modified.coeff.array
array of model coefficients sent to the function ’tableGlm’ to produce the final
output
x
object obtained from these ’display’ functions
...
further arguments passed to or used by methods
Details
R provides several epidemiological modelling techniques. The functions above display these results
in a format easier for medical people to understand.
The function ’tableGlm’ is not for general use. It is called by other display functions to receive the
’modified.coeff.array’ and produce the output table.
Risk.display
65
The argument ’simplified’ has a default value of ’FALSE’. It works best if the ’data’ argument has
been supplied during creation of the model. Under this condition, the output has three parts. Part
1 (the first line) indicates the type of the regression and the outcome. For logistic regression, if
the outcome is a factor then the referent level is shown. Part 2 shows the main output table where
each independent variable coefficient is displayed. If the independent variable is continuous (class
numeric) then name of the variable is shown (or the descriptive label if it exists). If the variable
is a factor then the name of the level is shown with the referent level omitted. In this case, the
name of the referent level and the statistic testing for group effects are displayed. An F-test is used
when the model is of class ’lm’ or ’glm’ with ’family=gaussian’ specified. A Likelihood Ratio
test is performed when the model is of class ’glm’ with ’family = binomial’ or ’family = poisson’
specified and for models of class ’coxph’ and ’clogit’. These tests are carried out with the records
available in the model, not necessary all records in the full ’data’ argument. The number of records
in the model is displayed in the part 3 of the output. When ’simplified=TRUE’, the first and the last
parts are omitted from the display.
The result is an object of class ’display’ and ’list’. Their apparence on the R console is controlled by
’print.display’. The ’table’ attribute of these ’display’ objects are ready to write (using ’write.csv’)
to a .csv file which can then be copied to a manuscript document. This approach can substantially
reduce both the time and errors produced due to conventional manual copying.
Value
’logistic.display’, ’regress.display’, ’clogit.display’ and ’cox.display’, each produces an output table. See ’details’.
Note
Before using these ’display’ functions, please note the following limitations.
1) Users should define the ’data’ argument of the model.
2) The names of the independent variables must be a subset of the names of the variables in the
’data’ argument. Sometimes, one of more variables are omitted by the model due to collinearity. In
such a case, users have to specify ’simplified=TRUE’ in order to get the display function to work.
3) Under the following conditions, ’simplified’ will be forced to TRUE and ’crude’ forced to
FALSE.
3.1) The names of the independent variables contain a function such as ’factor()’ or any ’\$’ sign.
3.2) The levels of the factor variables contain any ’:’ sign.
3.3) There are more than one interaction terms in the model
3.4) The ’data’ argument is missing in the conditional logistic regression and Cox regression model
4) For any other problems with these display results, users are advised to run ’summary(model)’
or ’summary(model)$coefficients’ to check the consistency between variable names in the model
and those in the coefficients. The number in the latter may be fewer than that in the former due
to collinearity. In this case, it is advised to specify ’simplified=TRUE’ to turn off the attempt to
tidy up the rownames of the output from ’summary(model)$coeffients’. The output when ’simplified=TRUE’ is more reliable but less understandable.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
66
Risk.display
See Also
’glm’, ’confint’
Examples
model0 <- glm(case ~ induced + spontaneous, family=binomial, data=infert)
summary(model0)
logistic.display(model0)
data(ANCdata)
glm1 <- glm(death ~ anc + clinic, family=binomial, data=ANCdata)
logistic.display(glm1)
logistic.display(glm1, simplified=TRUE)
library(MASS) # necessary for negative binomial regression
data(DHF99); .data <- DHF99
attach(.data)
model.poisson <- glm(containers ~ education + viltype,
family=poisson, data=DHF99)
model.nb <- glm.nb(containers ~ education + viltype,
data=.data)
idr.display(model.poisson) -> poiss
print(poiss) # or print.display(poiss) or poiss
idr.display(model.nb)
detach(.data)
data(VC1to6)
.data <- VC1to6
.data$fsmoke <- factor(.data$smoking)
levels(.data$fsmoke) <- list("no"=0, "yes"=1)
clr1 <- clogit(case ~ alcohol + fsmoke + strata(matset), data=.data)
clogistic.display(clr1)
rm(list=ls())
data(BP)
.data <- BP
attach(.data)
age <- as.numeric(as.Date("2000-01-01") - birthdate)/365.25
agegr <- pyramid(age,sex, bin=20)$ageGroup
.data$hypertension <- sbp >= 140 | dbp >=90
detach(.data)
model1 <- glm(hypertension ~ sex + agegr + saltadd, family=binomial,
data=.data)
logistic.display(model1) -> table3
attributes(table3)
table3
table3$table
# You may want to save table3 into a spreadsheet
write.csv(table3$table, file="table3.csv") # Note $table
## Have a look at this file in Excel, or similar spreadsheet program
ROC
67
file.remove(file="table3.csv")
model2 <- glm(hypertension ~ sex * age + sex * saltadd, family=binomial,
data=.data)
logistic.display(model2)
# More than 1 interaction term so 'simplified turned to TRUE
reg1 <- lm(sbp ~ sex + agegr + saltadd, data=.data)
regress.display(reg1)
reg2 <- glm(sbp ~ sex + agegr + saltadd, family=gaussian, data=.data)
regress.display(reg2)
data(Compaq)
cox1 <- coxph(Surv(year, status) ~ hospital + stage * ses, data=Compaq)
cox.display(cox1, crude.p.value=TRUE)
# Ordinal logistic regression
library(nnet)
options(contrasts = c("contr.treatment", "contr.poly"))
house.plr <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
house.plr
ordinal.or.display(house.plr)
# Polytomous or multinomial logistic regression
house.multinom <- multinom(Sat ~ Infl + Type + Cont, weights = Freq,
data = housing)
summary(house.multinom)
mlogit.display(house.multinom, alpha=.01) # with 99% confidence limits.
ROC
ROC curve
Description
Receiver Operating Characteristic curve of a logistic regression model and a diagnostic table
Usage
lroc(logistic.model, graph = TRUE, add = FALSE, title = FALSE,
line.col = "red", auc.coords = NULL, grid = TRUE, grid.col = "blue", ...)
roc.from.table(table, graph = TRUE, add = FALSE, title = FALSE,
line.col = "red", auc.coords = NULL, grid = TRUE, grid.col = "blue", ...)
Arguments
logistic.model A model from logistic regression
table
A cross tabulation of the levels of a test (rows) vs a gold standard positive and
negative (columns)
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ROC
graph
Draw ROC curve
add
Whether the line is drawn on the existing ROC curve
title
If true, the model will be displayed as main title
line.col
Color of the line
auc.coords
Coordinates for label of ’auc’ (area under curve)
grid
Whether the grid should be drawn
grid.col
Grid colour, if drawn
...
Additional graphic parameters
Details
’lroc’ graphs the ROC curve of a logistic regression model. If ‘table=TRUE’, the diagnostic table
based on the regression will be printed out.
’roc.from.table’ computes the change of sensitivity and specificity of each cut point and uses these
for drawing the ROC curve.
In both cases, the area under the curve is computed.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’glm’
Examples
# Single ROC curve from logistic regression
# Note that 'induced' and 'spontaneous' are both originally continuous variables
model1 <- glm(case ~ induced + spontaneous, data=infert, family=binomial)
logistic.display(model1)
# Having two spontaneous abortions is quite close to being infertile!
# This is actually not a causal relationship
lroc(model1, title=TRUE, auc.coords=c(.5,.1))
# For PowerPoint presentation, the graphic elements should be enhanced as followed
lroc(model1, title=TRUE, cex.main=2, cex.lab=1.5, col.lab="blue", cex.axis=1.3,
lwd=3)
lroc1 <- lroc(model1) # The main title and auc text have disappeared
model2 <- glm(case ~ spontaneous, data=infert, family=binomial)
logistic.display(model2)
lroc2 <- lroc(model2, add=TRUE, line.col="brown", lty=2)
legend("bottomright",legend=c(lroc1$model.description, lroc2$model.description),
lty=1:2, col=c("red","brown"), bg="white")
title(main="Comparison of two logistic regression models")
lrtest(model1, model2)
# Number of induced abortions is associated with increased risk for infertility
sampsize
69
# Various form of logistic regression
# Case by case data
data(ANCdata)
.data <- ANCdata
glm1 <- glm(death ~ anc + clinic, binomial, data=.data) # Note 'calc'
lroc(glm1)
# Frequency format
data(ANCtable)
ANCtable
.data <- ANCtable
attach(.data)
death <- factor (death)
levels (death) <- c("no","yes")
anc <- with(.data, factor (anc))
levels (anc) <- c("old","new")
clinic <- with(.data, factor (clinic))
levels (clinic) <- c("A","B")
.data <- data.frame(death, anc, clinic)
.data
glm2 <- glm(death ~ anc + clinic, binomial, weights=Freq, data=.data)
lroc(glm2)
detach(.data)
# ROC from a diagnostic table
table1 <- as.table(cbind(c(1,27,56,15,1),c(0,0,10,69,21)))
colnames(table1) <- c("Non-diseased", "Diseased")
rownames(table1) <- c("15-29","30-44","45-59","60-89","90+")
table1
roc.from.table(table1)
roc.from.table(table1, title=TRUE, auc.coords=c(.4,.1), cex=1.2)
# Application of the returned list
roc1 <- roc.from.table(table1, graph=FALSE)
cut.points <- rownames(roc1$diagnostic.table)
text(x=roc1$diagnostic.table[,1], y=roc1$diagnostic.table[,2],
labels=cut.points, cex=1.2, col="brown")
rm(list=ls())
sampsize
Sample size calculation
Description
Sample size calculations for epidemiological studies
Usage
n.for.survey (p, delta = "auto", popsize = NULL, deff = 1, alpha = 0.05)
n.for.2means (mu1, mu2, sd1, sd2, ratio = 1, alpha = 0.05, power = 0.8)
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sampsize
n.for.cluster.2means (mu1, mu2, sd1, sd2, alpha = 0.05, power = 0.8, ratio = 1,
mean.cluster.size = 10, previous.mean.cluster.size = NULL,
previous.sd.cluster.size = NULL, max.cluster.size = NULL, min.cluster.size =
NULL, icc = 0.1)
n.for.2p (p1, p2, alpha = 0.05, power = 0.8, ratio = 1)
n.for.cluster.2p (p1, p2, alpha = 0.05, power = 0.8, ratio = 1,
mean.cluster.size = 10, previous.mean.cluster.size = NULL,
previous.sd.cluster.size = NULL, max.cluster.size = NULL,
min.cluster.size = NULL, icc = 0.1)
n.for.equi.2p(p, sig.diff, alpha=.05, power=.8)
n.for.noninferior.2p (p, sig.inferior, alpha = 0.05, power = 0.8)
n.for.lqas (p0, q = 0, N = 10000, alpha = 0.05, exact = FALSE)
Arguments
p
estimated probability
delta
difference between the estimated prevalence and one side of the 95 percent confidence limit (precision)
popsize
size of the finite population
deff
design effect for cluster sampling
alpha
significance level
mu1, mu2
estimated means of the two populations
sd1, sd2
estimated standard deviations of the two populations
ratio
n2/n1
mean.cluster.size
mean of the cluster size planned in the current study
previous.mean.cluster.size, previous.sd.cluster.size
mean and sd of cluster size from a previous study
max.cluster.size, min.cluster.size
maximum and minimum of cluster size in the current study
icc
intraclass correlation coefficient
p1, p2
estimated probabilities of the two populations
power
power of the study
sig.diff
level of difference consider as being clinically significant
sig.inferior
level of reduction of effectiveness as being clinically significant
p0
critical proportion beyond which the lot will be rejected
q
critical number of faulty pieces found in the sample, beyond which the lot will
be rejected
N
lot size
exact
whether the exact probability is to be computed
sampsize
71
Details
’n.for.survey’ is used to compute the sample size required to conduct a survey.
When ’delta="auto"’, delta will change according to the value of p. If 0.3 <= p <= 0.7, delta = 0.1.
If 0.1 <= p < .3, or 0.7< p <=0.9, then delta=.05. Finally, if p < 0.1, then delta = p/2. If 0.9 < p, then
delta = (1-p)/2.
When cluster sampling is employed, the design effect (deff) has to be taken into account.
’n.for.2means’ is used to compute the sample size needed for testing the hypothesis that the difference of two population means is zero.
’n.for.cluster.2means’ and ’n.for.cluster.2p’ are for cluster (usually randomized) controlled trial.
’n.for.2p’ is used to the compute the sample size needed for testing the hypothesis that the difference
of two population proportions is zero.
’n.for.equi.2p’ is used for equivalent trial with equal probability of success or fail being p for both
groups. ’sig.diff’ is a difference in probability considered as being clinically significant. If both
sides of limits of 95 percent CI of the difference are within +sig.diff or -sig.diff, there would be
neither evidence of inferiority nor of superiority of any arm.
’n.for.noninferior.2p’ is similar to ’n.for.equi.2p’ except if the lower limit of 95 percent CI of the
difference is higher than the sig.inferior level, the hypothesis of inferiority would be rejected.
For a case control study, p1 and p2 are the proportions of exposure among cases and controls.
For a cohort study, p1 and p2 are proportions of positive outcome among the exposed and nonexposed groups.
’ratio’ in a case control study is controls:case. In cohort and cross-sectional studies, it is nonexposed:exposed.
LQAS stands for Lot Quality Assurance Sampling. The sample size n is determined to test whether
the lot of a product has a defective proportion exceeding a critical proportion, p0. Out of the sample
tested, if the number of defective specimens is greater than q, the lot is considered not acceptable.
This concept can be applied to quality assurance processes in health care.
When any parameter is a vector of length > 5, a table of sample size by the varying values of
parameters is displayed.
Value
a list.
’n.for.survey’ returns an object of class "n.for.survey"
’n.for.2p’ returns an object of class "n.for.2p"
’n.for.2means’ returns an object of class "n.for.2means"
’n.for.lqas’ returns an object of class "n.for.lqas"
Each type of returned values consists of vectors of various parameters in the formula and the required sample size(s).
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
72
sampsize
References
Eldridge SM, Ashby D, Kerry S. 2006 Sample size for cluster randomized trials: effect of coefficient
of variation of cluster size and analysis method. Int J Epidemiol 35(5): 1292-300.
See Also
’power.for.2means’, ’power.for.2p’
Examples
# In a standard survey to determine the coverage of immunization needed using
# a cluster sampling technique on a population of approximately 500000, and
# an estimated prevalence of 70 percent, design effect is assumed to be 2.
n.for.survey( p = .8, delta = .1, popsize = 500000, deff =2) # 123 needed
# To see the effect of prevalence on delta and sample size
n.for.survey( p = c(.5, .6, .7, .8, .9, .95, .99))
# Testing the efficacy of measles vaccine in a case control study .
# The coverage in the non-diseased population is estimated at 80 percent.
# That in the diseased is 60 percent.
n.for.2p(p1=.8, p2=.6) # n1=n2=91 needed
# A randomized controlled trial testing cure rate of a disease of
# 90 percent by new drugs and 80 percent by the old one.
n.for.2p(p1=.9, p2=.8) # 219 subjects needed in each arm.
# To see the effect of p1 on sample size
n.for.2p(p1=seq(1,9,.5)/10, p2=.5) # A table output
# The same randomized trial to check whether the new treatment is 5 percent
# different from the standard treatment assuming both arms has a common
# cure rate of 85 percent would be
n.for.equi.2p(p=.85, sig.diff=0.05)
# 801 each.
# If inferior arm is not allow to be lower than -0.05 (5 percent less effective)
n.for.noninferior.2p(p=.85, sig.inferior=0.05) # 631 each.
#
#
#
#
#
A cluster randomized controlled trial to test whether training of village
volunteers would result in reduction of prevalence of a disease from 50 percent
in control villages to 30 percent in the study village with a cluster size
varies from 250 to 500 eligible subjects per village (mean of 350) and the
intraclass correlation is assumed to be 0.15
n.for.cluster.2p(p1=.5, p2=.3, mean.cluster.size = 350, max.cluster.size = 500,
min.cluster.size = 250, icc = 0.15)
setTitle
73
# A quality assurance to check whether the coding of ICD-10 is faulty
# by no more than 2 percent.The minimum sample is required.
# Thus any faulty coding in the sample is not acceptable.
n.for.lqas(p0 = .02, q=0, exact = TRUE) # 148 non-faulty checks is required
# to support the assurance process.
n.for.lqas(p0 = (1:10)/100, q=0, exact = FALSE)
setTitle
Setting the displayed language of Epicalc graph title
Description
Setting locale and internationalizing Epicalc graph title
Usage
setTitle(locale)
Arguments
locale
A string denoting international language of choice
Details
On calling ’library(epicalc)’, ’.locale()’ has an inital value of FALSE, ie. the titles of Epicalc’s
automatic graphs are displayed in the English language. ’setTitle’ has two effects. It selects the
locale and resets the hidden object ’.locale()’ to TRUE. The command internationalizes the title of
automatic graphs created by Epicalc according to ’locale’ given in the function’s argument.
If ’.locale()’ is TRUE, then the automatic graphs produced by Epicalc commands, such as ’summ(var)’
or ’tab1(var)’ or ’tabpct(var1,var2)’, will lookup a language conversion table for the graph title and
the title will be changed accordingly.
Internationalization of the title can be disabled by typing ’.locale(FALSE)’. This has no effect of
locale as a whole unless it is reset to English by issuing the command ’setTitle("English")’.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’Sys.setlocale’, ’Sys.getlocale’ and ’titleString’
74
shapiro.qqnorm
Examples
.data <- iris
attach(.data)
summ(Sepal.Length, by=Species)
setTitle("English")
dotplot(Sepal.Length, by=Species)
setTitle("Malay")
dotplot(Sepal.Length, by=Species)
setTitle("Spanish")
dotplot(Sepal.Length, by=Species)
detach(.data)
rm(.data)
shapiro.qqnorm
Qqnorm plots with Shapiro-Wilk’s test
Description
Quantile-normal plots with Shapiro-Wilk’s test result integrated
Usage
shapiro.qqnorm (x, ...)
Arguments
x
A numeric vector
...
Graphical parameters passed to ’par’
Details
To test a variable ’x’ against the normal distribution, a qqnorm plot is integrated with the ShapiroWilk test to enhance interpretation.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’shapiro.test’, ’qqnorm’, ’par’
Examples
x <- rnorm(10)
a <- LETTERS[1:10]
shapiro.qqnorm(x, pch=a, col="red")
qqline(x, lty=2, col="black")
Sleepiness
Sleepiness
75
Dataset on sleepiness in a workshop
Description
Sleepiness among participants in a workshop
Usage
data(Sleep3)
Format
A data frame with 15 observations on the following 8 variables.
id a numeric vector
gender a factor with levels male female
dbirth a Date vector for birth date
sleepy a numeric vector for any experience of sleepiness in the class: 0=no 1=yes
lecture a numeric vector for ever felt sleepy during a lecture: 0=no 1=yes
grwork a numeric vector for ever felt sleepy during a group work: 0=no 1=yes
kg a numeric vector
cm a numeric vector
Examples
data(Sleep3)
des(Sleep3)
statStack
Statistics of a continuous variable stratified by factors
Description
Compares the difference in means or medians of the levels of a factor or list of factors
Usage
statStack (cont.var, by, dataFrame, iqr="auto", var.labels = TRUE, decimal = 1,
assumption.p.value = .01)
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statStack
Arguments
cont.var
a single continuous variable in the data frame
by
a factor, or list of factors, each of length <code>nrow(dataFrame)</code>
iqr
to display median and inter-quartile range instead of mean and sd.
var.labels
use descriptions of the ’by’ variables if available
dataFrame
source data frame of the variables
decimal
number of digits displayed after decimal point
assumption.p.value
level of Bartlett’s test P value to judge whether the comparison and the test
should be parametric
Details
This function computes means/medians of a continuous variable in each level of the specified factor(s) and performs an appropriate statistical test.
The classes of the variable to compute statistics must be either ’integer’ or ’numeric’ why all ’by’
variables must be ’factor’.
Like in ’tableStack’, the argument ’iqr’ has a default value being "auto". Non-parametric comparison and test will be automatically chosen if Bartlette’s test P value is below the ’assumption.p.value’.Like in ’tableStack’, the default value for the ’iqr’ argument is "auto", which means
non-parametric comparison and test will be automatically chosen if the P-value from Bartlett’s test
is below the value of the ’assumption.p.value’ argument (0.01).
The user can force the function to perform a parametric test by setting ’iqr=NULL’ and to perform
a non-parametric test by setting ’iqr’ to the name or index of the continuous variable.
By default, ’var.labels=TRUE’ in order to give nice output.
Value
an object of class ’statStack’ and ’table’
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’tableStack’
Examples
statStack(Price, by=c(DriveTrain, Origin), dataFrame=Cars93)
statStack(Price, by=c(DriveTrain, Origin), dataFrame=Cars93, iqr=NULL)
Cars93$log10.Price <- log10(Cars93$Price)# added as the 28th variable
statStack(log10.Price, by=c(DriveTrain, Origin), dataFrame=Cars93)
statStack(log10.Price, by=c(DriveTrain, Origin), dataFrame=Cars93, iqr=28)
summ
77
rm(Cars93)
data(Compaq)
statStack(year, by=c(hospital, stage:ses), dataFrame=Compaq)
# Note that var.labels 'Age group' is displayed instead of var. name 'agegr'
summ
Summary with graph
Description
Summary of data frame in a convenient table. Summary of a variable with statistics and graph
Usage
summ(x, ...)
## Default S3 method:
summ(x, by=NULL, graph = TRUE, box = FALSE, pch = 18,
ylab = "auto", main = "auto", cex.X.axis = 1, cex.Y.axis = 1,
dot.col = "auto", ...)
## S3 method for class 'factor'
summ(x, by=NULL, graph=TRUE, ...)
## S3 method for class 'logical'
summ(x, by=NULL, graph=TRUE, ...)
## S3 method for class 'data.frame'
summ(x, ...)
Arguments
x
by
graph
box
pch
ylab
main
cex.X.axis
cex.Y.axis
dot.col
...
’x’ can be a data frame or a vector
a stratification variable, valid only when x is a vector
automatic plot (sorted dot chart) if ’x’ is a vector
add a boxplot to the graph (by=NULL)
plot characters
annotation on Y axis
main title of the graph
character extension scale of X axis
character extension scale of Y axis
colour(s) of plot character(s)
additional graph parameters
Details
For data frames, ’summ’ gives basic statistics of each variable in the data frame. The other arguments are ignored.
For single vectors, a sorted dot chart is also provided, if graph=TRUE (default).
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tab1
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’summary’, ’use’, ’des’
Examples
data(Oswego)
.data <- Oswego
summ(.data)
with(.data, summ(age))
with(.data, summ(age, box=TRUE))
with(.data, summ(age, dot.col="brown"))
with(.data, summ(age, by=sex))
# Changing dot colours
with(.data, summ(age, by=sex, dot.col = c("blue","orange")))
# Enlarging main title and other elements
with(.data, summ(age, by=sex, cex.main=1.5, cex.X.axis=1.5, cex.Y.axis=1.7))
# Free vector
summ(rnorm(1000))
summ((1:100)^2, by=rep(1:2, 50))
summ((1:100)^2, by=rep(c("Odd","Even"), 50), main="Quadratic distribution by odd and even numbers")
tab1
One-way tabulation
Description
One-way tabulation with automatic bar chart and optional indicator variables generation
Usage
tab1(x0, decimal = 1, sort.group = c(FALSE, "decreasing",
"increasing"), cum.percent = !any(is.na(x0)), graph = TRUE,
missing = TRUE, bar.values = c("frequency", "percent", "none"),
horiz = FALSE, cex = 1, cex.names = 1, main = "auto", xlab = "auto",
ylab = "auto", col = "auto", gen.ind.vars = FALSE, ...)
## S3 method for class 'tab1'
print(x, ...)
tab1
79
Arguments
x0
a variable
decimal
number of decimals for the percentages in the table
sort.group
pattern for sorting categories in the table and in the chart. Default is no sorting.
cum.percent
presence of cumulative percentage in the output table. Default is TRUE for a
variable without any missing values.
graph
whether a graph should be shown
missing
include the missing values category or <NA> in the graphic display
bar.values
include the value of frequency, percentage or none at the end of each bar
horiz
set the bar chart to horizontal orientation
cex
parameter for extension of characters or relative size of the bar.values
cex.names
character extension or relative scale of the name labels for the bars
main
main title of the graph
xlab
label of X axis
ylab
label of Y axis
col
colours of the bar
gen.ind.vars
whether the indicator variables will be generated
x
object of class ’tab1’ obtained from saving ’tab1’ results
...
further arguments passed to or used by other methods
Details
’tab1’ is an advanced one-way tabulation providing a nice frequency table as well as a bar chart.
The description of the variable is also used in the main title of the graph.
The bar chart is vertical unless the number of categories is more than six and any of the labels of
the levels consists of more than 8 characters or ’horiz’ is set to TRUE.
For table has less than categories, the automatic colour is "grey". Otherwise, the graph will be
colourful. The argument, ’col’ can be overwritten by the user.
The argument ’gen.ind.vars’ is effective only if x0 is factor.
Value
Output table
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’tabpct’, ’label.var’, ’table’, ’barplot’, ’model.matrix’
80
tableStack
Examples
tab1(state.division)
tab1(state.division, bar.values ="percent")
tab1(state.division, sort.group ="decreasing")
tab1(state.division, sort.group ="increasing")
tab1(state.division, col=c("chocolate","brown1","brown4"),
main="Number of states in each zone")
# For presentation, several 'cex' parameters should increase
tab1(state.division, col=c("chocolate","brown1","brown4"),
main="Number of states in each zone",
cex.main=1.7, cex.name=1.2, cex.axis=1.3, cex.lab=1.3)
data(Oswego)
.data <- Oswego
attach(.data)
tab1(ill) # Note the column of cumulative percentages in the table.
tab1(ill, cum.percent=FALSE)
tab1(chocolate)
# Due to missing values, cumulative percentages are now automatically turned off.
tab1(chocolate, cum.percent=TRUE)
# Slightly too many columns in text!
tab1(chocolate, missing=FALSE, bar.values="percent")
agegr <- cut(age, breaks=c(0,10,20,30,40,50,60,70,80))
tab1(agegr) # No need to start with 'calc' as it is outside .data
tab1(agegr, col="grey") # graphic output from older versions of 'tab1'
tab1(agegr, col=c("red","yellow","blue")) # Colours recycled
tab1(agegr, horiz=TRUE)
# Keeping output table
dev.off()
tab1(agegr, graph = FALSE) -> a
print(a)
a # same results
attributes(a)
a$output.table
class(a$output.table) # "matrix"
# 'a$output.table' is ready for exporting to a .csv file by
# write.csv(a$output.table, file="table1.csv")
# "table1.csv" is now readable by a spreadsheet program
detach(.data)
rm(list=ls())
tableStack
Tabulation of variables in a stack form
Description
Tabulation of variables with the same possible range of distribution and stack into a new table with
or without other descriptive statistics or to breakdown distribution of more than one row variables
against a column variable
tableStack
81
Usage
tableStack (vars, dataFrame, minlevel = "auto", maxlevel = "auto", count = TRUE,
na.rm =FALSE, means = TRUE, medians = FALSE, sds = TRUE, decimal = 1,
total = TRUE, var.labels = TRUE, var.labels.trunc =150, reverse = FALSE,
vars.to.reverse = NULL, by = NULL, vars.to.factor = NULL, iqr = "auto",
prevalence = FALSE, percent = c("column", "row", "none"), frequency=TRUE,
test = TRUE, name.test = TRUE, total.column = FALSE, simulate.p.value = FALSE,
sample.size=TRUE, assumption.p.value = .01)
Arguments
vars
a vector of variables in the data frame
dataFrame
source data frame of the variables
minlevel
possible minimum value of items specified by user
maxlevel
possible maximum value of items specified by user
count
whether number of valid records for each item will be displayed
na.rm
whether missing value would be removed during calculation mean score of each
person
means
whether means of all selected items will be displayed
medians
whether medians of all selected items will be displayed
sds
whether standard deviations of all selected items will be displayed
decimal
number of decimals displayed in the statistics
total
display of means and standard deviations of total and average scores
var.labels
presence of descriptions of variables on the last column of output
var.labels.trunc
number of characters used for variable description
reverse
whether item(s) negatively correlated with other majority will be reversed
vars.to.reverse
variable(s) to reverse
by
a variable for column breakdown. If a single character (with quotes) is given,
only the ’total column’ will be displayed
vars.to.factor variable(s) to be converted to factor for tabulaton
iqr
variable(s) to display median and inter-quartile range
prevalence
for logical variable, whether prevalence of the dichotomous row variable in each
column subgroup will be displayed
percent
type of percentage displayed when the variable is categorical. Default is column
frequency
whether to display frequency in the cells when the variable is categorical
test
whether statistical test(s) will be computed
name.test
display name of the test and relevant degrees of freedom
total.column
whether to add ’total column’ to the output or not
82
tableStack
simulate.p.value
simulate P value for Fisher’s exact test
sample.size
whether to display non-missing sample size of each column
assumption.p.value
level of Bartlett’s test P value to judge whether the comparison and the test
should be parametric
Details
This function simultaneously explores several variables with a fixed integer rating scale. For nonfactor variables, the default values for tabulation are the minimum and the maximum of all variables
but can be specified by the user.
When ’by’ is omitted, all variables must be of the same class, and must be ’integer’, ’factor’ or
’logical.
Unlike function ’alpha’, the argument ’reverse’ has a default value of FALSE. This argument is
ignored if ’vars.to.reverse’ is specified.
Options for ’reverse’, ’vars.to.reverse’ and statistics of ’means’, ’medians’, ’sds’ and ’total’ are
available only if the items are not factor. To obtain statistics of factor items, users need to use
’unclassDataframe’ to convert them into integer.
When the ’by’ argument is given, ’reverse’ and ’vars.to.reverse’ do not apply. Instead, columns
of the ’by’ variable will be formed. A table will be created against each selected variable. If
the variable is a factor or coerced to factor with ’vars.to.factor’, cross-tabulation will result with
percents as specified, ie. "column", "row", or "none" (FALSE). For a dichotomous row variable,
if set to ’TRUE’, the prevalence of row variable in the form of a fraction is displayed in each
subgroup column. For objects of class ’numeric’ or ’integer’, means with standard deviations will
be displayed. For variables with residuals that are not normally distributed or where the variance
of subgroups are significantly not normally distributed (using a significance level of 0.01), medians
and inter-quartile ranges will be presented if the argument ’iqr’ is set to "auto" (by default). Users
may specify a subset of the selected variables (from the ’vars’ argument) to be presented in such a
form. Otherwise, the argument could be set as any other character string such as "none", to insist to
present means and standard deviations.
When ’test = TRUE’ (default), Pearson’s chi-squared test (or a two-sided Fisher’s exact test, if the
sample size is small) will be carried out for a categorical variable or a factor. Parametric or nonparametric comparison and test will be carried out for a object of class ’numeric’ or ’integer’ (See
’iqr’ and ’assumption.p.value’ below). If the sample size of the numeric variable is too small in any
group, the test is omitted and the problem reported.
For Fisher’s exact test, the default method employs ’simulate.p.value = FALSE’. See further explanation in ’fisher.test’ procedure. If the dataset is extraordinarily large, the option may be manually
set to TRUE.
When ’by’ is specified as a single character object (such as ’by="none"’), there will be no column
breakdown and all tests will be omitted. Only the total column is displayed. Only the ’total’ column
is shown.
If this ’total column’ is to accompany the ’by’ breakdown, the argument ’total.column=TRUE’
should be specified. The ’sample.size’ is TRUE by default. The total number of records for each
group is displayed in the first row of the output. However, the variable in each row may have some
missing records, the information on which is not reported by tableStack.
tableStack
83
By default, Epicalc sets ’var.labels=TRUE’ in order to give nice output. However, ’var.labels=FALSE’
can sometimes be more useful during data exploration. Variable numbers as well as variable names
are displayed instead of variable labels. Names and numbers of abnormally distributed variables,
especially factors with too many levels, can be easily identified for further relevelling or recoding.
The argument ’iqr’ has a default value being "auto". Non-parametric comparison and test will be
automatically chosen if Bartlett’s test P value is below the ’assumption.p.value’.
The test can be forced to parametric by setting ’iqr=NULL’ and to non-parametric by if iqr is set to
the variable number of cont.var (See examples.).
Value
an object of class ’tableStack’ and ’list’ when by=NULL
results
an object of class ’noquote’ which is used for print out
items.reversed name(s) of variable(s) reversed
total.score
a vector from ’rowSums’ of the columns of variables specified in ’vars’
mean.score
a vector from ’rowMeans’ of the columns of variables specified in ’vars’
mean.of.total.scores
mean of total scores
sd.of.total.scores
standard deviation of total scores
mean.of.average.scores
mean of mean scores
sd.of.average.scores
standard deviation of mean scores
When ’by’ is specified, an object of class ’tableStack’ and ’table is returned.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’table’, ’tab1’, ’summ’, ’alpha’, ’unclassDataframe’
Examples
data(Oswego)
tableStack(bakedham:fruitsalad,
.data <- Oswego
des(.data)
attach(.data)
tableStack(bakedham:fruitsalad,
tableStack(bakedham:fruitsalad,
tableStack(bakedham:fruitsalad,
tableStack(bakedham:fruitsalad,
tableStack(bakedham:fruitsalad,
detach(.data)
dataFrame=Oswego)
.data)
.data,
.data,
.data,
.data,
# Default data frame is .data
by= ill)
by= ill, prevalence=TRUE)
by= ill, percent=FALSE)
by= ill, percent=FALSE, name.test=FALSE)
84
tableStack
data(Cars93, package="MASS")
.data <- Cars93
des(.data)
tableStack(vars=4:25, .data, by=Origin)
tableStack(vars=4:25, .data, by="none")
tableStack(vars=4:25, .data, by=Origin, total.column=TRUE)
data(Attitudes)
.data <- Attitudes
attach(.data)
tableStack(qa1:qa18, .data) # May need full screen of Rconsole
tableStack(qa1:qa18, .data, var.labels.trunc=35)
# Fits in with default R console screen
tableStack(qa1:qa18, .data, reverse=TRUE) -> a
a
## Components of 'a' have appropriate items reversed
a$mean.score -> mean.score
a$total.score -> total.score
.data$mean.score <- mean.score
.data$total.score <- total.score
rm(total.score, mean.score)
detach(.data)
attach(.data)
tableStack(c(qa1,qa13:qa18,mean.score,total.score), .data, by=sex, test=FALSE)
tableStack(c(qa15, qa17, mean.score:total.score), .data, by=sex, iqr=c(qa17,total.score))
tableStack(c(qa15, qa17, mean.score:total.score), .data, by=dep, iqr=c(qa17,total.score))
## 'vars' can be mixture of different classes of variables
.data$highscore <- mean.score > 4
tableStack(mean.score:highscore, .data, by=sex, iqr=total.score)
detach(.data)
rm(list=ls())
data(Ectopic)
.data <- Ectopic
des(.data)
tableStack(vars=3:4,
tableStack(vars=3:4,
tableStack(vars=3:4,
tableStack(vars=3:4,
.data,
.data,
.data,
.data,
by=outc)
by=outc, percent="none")
by=outc, prevalence = TRUE)
by=outc, name.test = FALSE)
## Variable in numeric or factor
data(Outbreak)
.data <- Outbreak
des(.data)
# Comparison of exposure to food items between the two gender
tableStack(vars=5:8, .data, by=sex) # as continuous varaibles
tableStack(vars=5:8, .data, by=sex, vars.to.factor = 5:8) # as factors
tabpct
tabpct
85
Two-way tabulation with mosaic plot
Description
Two-way tabulation with automatic mosaic plot
Usage
tabpct(row, column, decimal = 1, percent = c("both", "col",
"row"), graph = TRUE, las = 0, main = "auto", xlab = "auto",
ylab = "auto", col = "auto", ...)
Arguments
row, column
variables
decimal
number of decimals for the percentage in the table
percent
orientation of the percentage in the table
graph
automatic graphing
las
orientation of group labelling
main
main title
xlab
X axis label
ylab
Y axis label
col
colours of the bars
...
additional arguments for ’table’
0: always parallel to axis
1: always horizontal,
2: always perpendicular to the axis,
3: always vertical.
Details
’tabpct’ gives column and row percent cross-tabulation as well as mosaic plot.
The width of the bar in the plot denotes the relative proportion of the row variable.
Inside each bar, the relative proportion denotes the distribution of column variables within each row
variable.
Note that ’row’ and ’col’ arguments of this function are for the table, not the mosaic plot and the
default value for the ’percent’ orientation is "both".
Due to limitation of ’mosaicplot’, certain graphic parameters such as ’cex.main’, ’cex.lab’ are not
acceptable. The parameter ’main’, ’xlab’ and ’ylab’ can be suppressed by making equal to " ". An
additional line starting with ’title’ can be used to write new main and label titles with ’cex.main’
and ’cex.lab’ specified.
86
Timing exercise
Value
Tables of row and column percentage
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’tab1’, ’table’, ’mosaicplot’
Examples
data(Oswego)
.data <- Oswego
attach(.data)
agegr <- cut(age, breaks=c(0,20,40,60,80))
tabpct(agegr, ill)
tabpct(agegr, ill, cex.axis=1) # enlarge value labels
# To increase the size of the various titles:
tabpct(agegr, ill, cex.axis=1, main="", xlab="", ylab="", col=c("blue","purple"))
title(main="Diseased by Age group", cex.main=1.8,
xlab="Age (years)",ylab="Diseased", cex.lab=1.5)
detach(.data)
rm(list=ls())
Timing exercise
Dataset on time going to bed, waking up and arrival at a workshop
Description
This dataset came from an interview survey on the workshop attendants on 2004-12-14.
Note that the date of bed time is 2004-12-13 if the respondent went to bed before midnight.
Usage
data(Timing)
Format
A data frame with 18 observations on the following 11 variables.
id a numeric vector
gender a factor with levels male female
age a numeric vector
marital a factor with levels single married others
child a numeric vector indicating number of children
titleString
87
bedhr a numeric vector indicating the hour of going to bed
bedmin a numeric vector indicating the minute of going to bed
wokhr a numeric vector indicating the hour of waking up
wokmin a numeric vector indicating the minute of waking up
arrhr a numeric vector indicating the hour of arrival at the workshop
arrmin a numeric vector indicating the minute of arrival at the workshop
Examples
data(Timing)
des(Timing)
titleString
Replace commonly used words in Epicalc graph title
Description
Setting vocabularies for Epicalc graph title
Usage
titleString (distribution.of = .distribution.of, by = .by,
frequency = .frequency, locale = .locale(),
return.look.up.table=FALSE)
Arguments
distribution.of
A string denoting "Distribution of"
by
That for "by"
frequency
That for "Frequency"
locale
Logical value to overwrite .locale(). The initial value is FALSE
return.look.up.table
Should the look-up table be returned?
Details
The two internationalization commands of Epicalc, ’setTitle’ and ’titleString’, work together to set
the langauge and wording of titles of automatic graphs obtained from certain Epicalc functions.
In general, ’setTitle’ is simple and works well if the locale required fits in with the version of the
operating system. The three commonly used words in the graph titles: "Distribution of", "by" and
"Frequency", which are in English, are initially stored in three respective hidden objects ’.distribution.of’, ’.by’ and ’.frequency’ as well as in the look-up table within the ’titleString’ function.
When the locale is changed to a language other than English, the look-up table is used and wordings
are changed accordingly.
88
titleString
The function ’titleString’ is useful when the user wants to change the strings stored in the look-up
table. It changes the initial values of ’.distribution.of’, ’.by’ and ’.frequency’, respectively. The
argument, ’locale’, must be manually set to FALSE by the user to disable the use of the look-up
table and to enable the use of the three objects assigned by the command instead.
The two functions suppress each other. Use of ’setTitle’ disables the effects of ’titleString’, switching .locale() to TRUE and forcing Epicalc to read from the look-up table in ’titleString’. However,
’setTitle’ does not overwrite the values assigned by the arguments of ’titleString’.
The key and decisive switch object is .locale(). Once .locale() is set to FALSE, either manually
or inside the ’titleString’ command, the values of the three hidden objects will be used. Setting
.locale() to TRUE, either manually or automatically by the ’setTitle’ function, points the graph title
to use the look-up table inside ’titleString’.
Typing ’titleString()’ without an argument displays the current contents of these three objects. The
look-up table is also displayed if the return.look.up.table argument is set to TRUE.
International users who want to add their specific locales and corresponding terminology to the
look-up table or to suggest more appropriate terminology can contact the author.
Author(s)
Virasakdi Chongsuvivatwong < <[email protected]>>
See Also
’setTitle’
Examples
.data <- iris
attach(.data)
dotplot(Sepal.Length, by=Species)
titleString(distribution.of="", by="grouped by", locale=FALSE)
## The above command is equivalent to the following three lines:
## .distribution.of <- ""
## .by <- "grouped by"
## .locale(FALSE)
dotplot(Sepal.Length, by=Species)
titleString()
setTitle("English")
dotplot(Sepal.Length, by=Species)
titleString(return.look.up.table=TRUE)
.locale(FALSE)
dotplot(Sepal.Length, by=Species)
titleString()
.distribution.of <- "Distribution of"
titleString()
Tooth decay
89
.by <- "by"
titleString()
detach(.data)
rm(.data)
Tooth decay
Dataset on tooth decay and mutan streptococci
Description
Relationship between bacteria and presence of any decayed tooth.
Usage
data(Decay)
Format
A data frame with 436 observations on the following 2 variables.
decay a numeric vector indicating presence of tooth decay
strep a numeric vector indicating number of colony-forming-units (CFUs) of Streptococcus mutan
in the saliva
Source
Teanpaisan, R., Kintarak, S., Chuncharoen, C., Akkayanont, P. 1995 Mutans Streptococci and dental -caries in schoolchildren in Southern Thailand. Community Dentistry and Oral Epidemiology
23: 317-318.
Examples
data(Decay)
des(Decay)
90
Xerophthalmia and respiratory infection
Voluntary counselling and testing
Dataset on attitudes toward VCT
Description
This dataset contains information on the records of 200 women working at a tourist destination
community.
Usage
data(VCT)
Format
A subset of a data frame containing 200 observations and 12 variables with variable descriptions.
Details of the codes can be seen from the results of the function ’codebook()’ below.
Examples
data(VCT)
codebook(VCT)
Xerophthalmia and respiratory infection
Dataset from an Indonesian study on vitamin A deficiency and risk of
respiratory infection
Description
This dataset was adopted from Diggle et al: Analysis of Longitudinal Data. REFERENCE – Zeger
and Karim, JASA (1991)
Note that there are some duplications of id and time combination.
Usage
data(Xerop)
Xerophthalmia and respiratory infection
Format
A data frame containing 1200 observations and 10 variables.
id a numeric vector for personal identification number
respinfect whether the child had respiratory infection in that visit
age.month current age in month
xerop whether the child currently had vitamin A deficiency
sex gender of the child no detail on the code
ht.for.age height for age
stunted whether the child has stunted growth
time time of scheduled visit
baseline.age baseline age
season season
Examples
data(Xerop)
91
Index
print power.for.2p, 58
print statStack, 59
print summ.data.frame, 60
print summ.default, 60
print tableStack, 61
Risk.display, 63
setTitle, 73
summ, 77
∗Topic datasets
Age at marriage, 3
Air Pollution, 9
ANC Table, 12
Antenatal care data, 12
Attitudes dataset, 13
Bangladesh Fertility Survey, 14
Blood pressure, 14
Cancer survival, 15
Data for cleaning, 22
DHF99, 24
Ectopic pregnancy, 27
Familydata, 28
Hakimi’s data, 31
Hookworm 1993, 32
Hookworm and blood loss, 32
IUD trial admission data, 33
IUD trial discontinuation data, 34
IUD trial follow-up data, 34
Matched case-control study, 40
Montana, 43
Oswego, 44
Outbreak investigation, 44
Sleepiness, 75
Timing exercise, 86
Tooth decay, 89
Voluntary counselling and testing,
90
Xerophthalmia and respiratory
infection, 90
∗Topic htest
∗Topic aplot
dotplot, 25
Follow-up Plot, 29
pyramid, 62
statStack, 75
tab1, 78
tableStack, 80
tabpct, 85
∗Topic array
cc, 16
kap, 35
matchTab, 41
mhor, 42
ROC, 67
∗Topic database
aggregate numeric, 4
aggregate plot, 6
alpha, 9
CI, 18
Codebook, 21
des, 23
List non-function objects, 37
lookup, 38
print alpha, 48
print cci, 48
print des, 49
print kap.ByCategory, 50
print kap.table, 50
print lrtest, 51
print matchTab, 52
print n.for.2means, 52
print n.for.2p, 53
print n.for.cluster.2means, 54
print n.for.cluster.2p, 54
print n.for.equi.2p, 55
print n.for.lqas, 56
print n.for.noninferior.2p, 56
print n.for.survey, 57
print power.for.2means, 58
92
INDEX
lrtest, 39
poisgof, 46
shapiro.qqnorm, 74
∗Topic math
Power, 47
sampsize, 69
∗Topic misc
titleString, 87
Age at marriage, 3
aggregate numeric, 4
aggregate plot, 6
aggregate.numeric (aggregate numeric), 4
aggregate.plot (aggregate plot), 6
Air Pollution, 9
alpha, 9
alphaBest (alpha), 9
ANC Table, 12
ANCdata (Antenatal care data), 12
ANCtable (ANC Table), 12
Antenatal care data, 12
Attitudes (Attitudes dataset), 13
Attitudes dataset, 13
Bang (Bangladesh Fertility Survey), 14
Bangladesh Fertility Survey, 14
Blood pressure, 14
BP (Blood pressure), 14
Cancer survival, 15
cc, 16
cci (cc), 16
CI, 18
ci (CI), 18
clogistic.display (Risk.display), 63
Codebook, 21
codebook (Codebook), 21
Compaq (Cancer survival), 15
cox.display (Risk.display), 63
cs (cc), 16
csi (cc), 16
Data for cleaning, 22
Decay (Tooth decay), 89
des, 23
DHF99, 24
dotplot, 25
Ectopic (Ectopic pregnancy), 27
93
Ectopic pregnancy, 27
Familydata, 28
Follow-up Plot, 29
followup.plot (Follow-up Plot), 29
graph.casecontrol (cc), 16
graph.prospective (cc), 16
Hakimi (Hakimi’s data), 31
Hakimi’s data, 31
Hookworm 1993, 32
Hookworm and blood loss, 32
HW93 (Hookworm 1993), 32
idr.display (Risk.display), 63
IUD trial admission data, 33
IUD trial discontinuation data, 34
IUD trial follow-up data, 34
IudAdmit (IUD trial admission data), 33
IudDiscontinue (IUD trial
discontinuation data), 34
IudFollowup (IUD trial follow-up data),
34
kap, 35
labelTable (cc), 16
List non-function objects, 37
logistic.display (Risk.display), 63
lookup, 38
lroc (ROC), 67
lrtest, 39
lsNoFunction (List non-function
objects), 37
make2x2 (cc), 16
Marryage (Age at marriage), 3
Matched case-control study, 40
matchTab, 41
mhor, 42
mlogit.display (Risk.display), 63
Montana, 43
n.for.2means (sampsize), 69
n.for.2p (sampsize), 69
n.for.cluster.2means (sampsize), 69
n.for.cluster.2p (sampsize), 69
n.for.equi.2p (sampsize), 69
n.for.lqas (sampsize), 69
94
n.for.noninferior.2p (sampsize), 69
n.for.survey (sampsize), 69
ordinal.or.display (Risk.display), 63
Oswego, 44
Outbreak (Outbreak investigation), 44
Outbreak investigation, 44
Planning (Data for cleaning), 22
poisgof, 46
Power, 47
power.for.2means (Power), 47
power.for.2p (Power), 47
print alpha, 48
print cci, 48
print des, 49
print kap.ByCategory, 50
print kap.table, 50
print lrtest, 51
print matchTab, 52
print n.for.2means, 52
print n.for.2p, 53
print n.for.cluster.2means, 54
print n.for.cluster.2p, 54
print n.for.equi.2p, 55
print n.for.lqas, 56
print n.for.noninferior.2p, 56
print n.for.survey, 57
print power.for.2means, 58
print power.for.2p, 58
print statStack, 59
print summ.data.frame, 60
print summ.default, 60
print tableStack, 61
print.alpha (print alpha), 48
print.cci (print cci), 48
print.des (print des), 49
print.display (Risk.display), 63
print.kap.ByCategory (print
kap.ByCategory), 50
print.kap.table (print kap.table), 50
print.lrtest (print lrtest), 51
print.matchTab (print matchTab), 52
print.n.for.2means (print
n.for.2means), 52
print.n.for.2p (print n.for.2p), 53
print.n.for.cluster.2means (print
n.for.cluster.2means), 54
INDEX
print.n.for.cluster.2p (print
n.for.cluster.2p), 54
print.n.for.equi.2p (print
n.for.equi.2p), 55
print.n.for.lqas (print n.for.lqas), 56
print.n.for.noninferior.2p (print
n.for.noninferior.2p), 56
print.n.for.survey (print
n.for.survey), 57
print.power.for.2means (print
power.for.2means), 58
print.power.for.2p (print
power.for.2p), 58
print.statStack (print statStack), 59
print.summ.data.frame (print
summ.data.frame), 60
print.summ.default (print
summ.default), 60
print.tab1 (tab1), 78
print.tableStack (print tableStack), 61
pyramid, 62
regress.display (Risk.display), 63
Risk.display, 63
ROC, 67
roc.from.table (ROC), 67
sampsize, 69
setTitle, 73
shapiro.qqnorm, 74
Sleep3 (Sleepiness), 75
Sleepiness, 75
SO2 (Air Pollution), 9
statStack, 75
summ, 77
Suwit (Hookworm and blood loss), 32
tab1, 78
tableGlm (Risk.display), 63
tableStack, 80
tabpct, 85
Timing (Timing exercise), 86
Timing exercise, 86
titleString, 87
Tooth decay, 89
VC1to1 (Matched case-control study), 40
VC1to6 (Matched case-control study), 40
VCT (Voluntary counselling and
testing), 90
INDEX
Voluntary counselling and testing, 90
Xerop (Xerophthalmia and respiratory
infection), 90
Xerophthalmia and respiratory
infection, 90
95